Color constancy algorithms can provide us with illuminant invariant descriptions for a scene, and it is often accomplished by illuminant estimation. Most statistics-based methods estimate the illuminant color from the information provided by all pixels of an image. However, this research reveals that, for most images, the color of many pixels is quite different from the illuminant, and these pixels

Multiview learning is an important method and widely used for feature fusion in the fields of image process or big data analysis. Determining how to integrate compatible and complementary information from multiple views is a crucial and challenging task. We present a multiview feature fusion optimization method for image retrieval based on matrix correlation. This method first extracts four view features

In recent years, helping individuals with color vision deficiency to distinguish confusing colors in digital images, which is called Daltonization, is a hot topic. However, a number of Daltonization methods have a color reduction problem that causes unnatural image colors for normal color vision observers and those with anomalous trichromatic color vision deficiencies. A color-enhancing algorithm is

Predicting bounding box with higher intersection over union (IoU) is one of the most important issues in many computer vision tasks. The ℓn-norm loss and IoU-based loss are two conventional approaches to guide a training process in recent methods. However, the optimization direction of ℓn-norm loss is not exactly the same as maximizing the metric. In addition, IoU-based loss suffers from some inevitable

Crowd density estimation is an important part of intelligent crowd monitoring. However, there are still many problems in density estimation due to the complexity of crowd scenes. Aiming at the high-density scenes with varied scales, we present a method based on cross-adversarial loss and global feature for crowd counting, so as to achieve the purpose of capturing more feature details and reducing the

Object detection for blind zones is critical to ensuring the driving safety of heavy trucks. We propose a scheme to realize object detection in the blind zones of heavy trucks based on the improved you-only-look-once (YOLO) v3 network. First, according to the actual detection requirements, the targets are determined to establish a new data set of persons, cars, and fallen pedestrians, with a focus

Human action recognition based on skeleton currently has attracted a wide range of attention. The structure of skeleton data exists in the form of graph, thus most researchers use graph convolutional networks (GCN) to model skeleton sequences. However, the graph convolution network shares the same weight for all neighbor nodes and relies on the connection of graph edges. We introduce a method, a spatial–temporal

We propose a method for hyperspectral image (HSI) super-resolution by designing a tensor singular value decomposition (t-SVD) and three-dimensional total variation (3D-TV) regularization terms. The super-resolution method is designed as an optimization problem whose cost function consists of a data-fidelity term, the low-rank representation term by t-SVD, and the 3D-TV regularization term. The sparse

Given a natural language description, description-based person re-identification aims to retrieve images of the matched person from a large-scale visual database. Due to the existing modality heterogeneity, it is challenging to measure the cross-modal similarity between images and text descriptions. Many of the existing approaches usually utilize a deep-learning model to encode local and global fine-grained

The ear has emerged as a new biometric trait to recognize humans from their profile faces. Stability over the years, noninvasive capturing process, expressionless images, and significant variation in shape among individuals make the ear a suitable choice when compared with other biometrics. Convolutional neural network (CNN)’s capability to learn and discriminate specific features irrespective of image

Head pose estimation is a typical computer vision task that has been applied to many helpful applications. It is still challenging due to occlusions, low resolution, and extreme pose changes. A method is proposed for landmark-free head pose estimation in a fusion inception deep neural network. The fusion inception network is developed to predict head pose angles without landmarks in RGB images only

This guest editorial introduces the Special Section on Quality Control by Artificial Vision VI.

With recent advances in human pose estimation and human skeleton capture systems, pose-based action recognition has drawn lots of attention among researchers. Although most existing action recognition methods are based on convolutional neural network and long short-term memory, which present outstanding performance, one of the shortcomings of these methods is that they lack the ability to explicitly

Diffusion-based inpainting can be used to repair some damaged parts or remove the undesirable regions in an image. Generally, good visual effects can be achieved after inpainting. However, some traces, such as the differences of local variances and noise pattern, are left in the inpainted image, making it easy for the forensic algorithms to locate the inpainted regions. To eliminate this drawback and

Multiple region proposal networks (RPNs) have been recently combined with the Siamese network with deeper backbone networks for tracking and shown excellent accuracy with high efficiency. Although the destruction of the strict translation invariance caused by network padding in the original ResNet-50 is solved by a custom sampling strategy, its impact is not eliminated from the network structure itself

Quality control is a key requirement of the Fourth Industrial Revolution, especially in manufacturing. For high-value technical parts, the geometric and appearance quality need to be guaranteed. The control of the part, directly on the manufacturing line, can be achieved with noncontact sensors. Visible spectrum imaging is commonly used. However, more information could be obtained from broader spectrum

The coloring efficiency of most colorization algorithms is suboptimal. Therefore, the dual feature extractor generative adversarial network was designed for colorization. The U-Net-like network was used as the trunk network in the generator. The encoder was used to extract the local features of a grayscale image. The branch extractor used the ResNeXt network that was added to the SE module as a high-level

Traditional face alignment based on machine learning usually tracks the localizations of facial landmarks employing a static model trained offline where all of the training data are available in advance. When new training samples arrive, the static model must be retrained from scratch, which is excessively time-consuming and memory-consuming. It results in the limitation of its performance on sequential

Plenoptic cameras attract extensive attention these days for their unique information acquisition and postcapture processing capability. In prior work, the decoding pipeline of plenoptic cameras mainly consisted of calibration, aligning, slicing, and resampling. Besides, slicing and resampling are performed on a generated grid of projected centers. Such methods consist of a series of processing steps

Defect detection is critical for product quality assessment. Currently, machine vision technology has gradually replaced inefficient manual inspections. Due to the intricate textures and assorted defects on the product surface, conventional defect detection technology still requires ameliorating. We propose an improved deep learning network for defect detection of magnetic sheets, which contains the

A deep learning algorithm for Gaussian noise removal from both grayscale and color images is developed. As opposed to most existing discriminative methods that train a specific model for each noise level, the proposed method can handle a wide range of noise levels using only two trained models, one for low noise levels and the other for high noise levels. In the proposed algorithm, the training process

When light propagates through water, there will be absorption and scattering. Due to this reason, underwater images suffer from color distortion, contrast loss, and detail blurriness. In order to improve the quality of underwater images, an underwater image restoration method based on adaptive color correction and adaptive transmission fusion strategy is proposed. First, an adaptive color correction

To secure the secret communication, a robust video steganography algorithm is proposed. The major objectives of the proposed method (PM) are: (1) perceiving the region of interest (ROI) keypoints’ locations in video frames for concealing the secret data and (2) determining the appropriate amount of data to be embedded into the perceived ROI keypoints’ region. In the PM, keypoints are initially extracted

It is well known that more than three or four spectral measurements are required for accurate measurement of color under arbitrary illumination. Previous work has shown 6 to 10 measurements can yield accurate results on average, but with significant numbers of errors above the threshold of obvious visual detection. We demonstrate an imaging system (LED box) consisting of a monochrome camera, multiple

In vehicle monitoring, recognizing graphic vehicle identification number (VIN) on the car frame is a particularly important step. While text recognition methods have made great progress, automatic graphic vehicle VIN recognition is still challenging. In VIN images, the VIN text is engraved on the car frame, with complex background and arbitrary orientation, which make it extremely difficult for recognition

Most existing dehazing approaches employ image priors such as a dark channel prior, which may be violated in the nighttime. In addition, these approaches perform dehazing directly on the original hazy image, which usually results in detail blurring and noise amplifying. To address both issues, an effective single nighttime image haze removal approach is presented. We first decompose the hazy image

Fusing the pertinence of natural scene statistics-based methods and the ubiquity of convolutional neural network-based methods, a no-reference image quality assessment (IQA) method fusing deep learning and statistical visual features for no-reference image quality assessment (FDSVIQA) is proposed. For the statistical visual features, a local normalized luminance map and a local normalized local binary

Image correspondence is established by “matching” the feature descriptors of the interest points in the target image to that of the reference image. By acceptance testing, we refer to a postmatching hypothesis test used to screen out potential false matches—conventionally using descriptor test statistics. We propose a new acceptance testing strategy that does not rely on the descriptor test statistics

The fast detection and classification of underwater targets is a key issue in the operation of intelligent underwater robots. In order to improve the detection speed of underwater targets and reduce the missed detection rate of small targets, an improved YOLOv3 algorithm named YOLOv3-Marine is proposed. The network parameters were reduced and the detection speed was increased due to improving the YOLOv3

Person reidentification (ReID) is an important issue in the field of image processing and computer vision. Because pedestrian images are often affected by various interference factors, such as occlusion, illumination changes, posture changes, and background changes, extracting discriminative features is an important method to improve the accuracy of ReID. Based on the two existing methods of pose-sensitive

In the field of pattern recognition and computer vision, applying the symmetric positive definite (SPD) matrix to represent an image set is widely studied, and the remarkable performance that it yields demonstrates its effectiveness. However, the computational burden of the original SPD matrices is usually high, which may restrict the applicability of existing methods. To address this problem, we proposed

An image detail enhancement algorithm is proposed based on in-place residual homogeneity (IP). Residual homogeneity is a physical law, which mainly explains the texture similarity between the same image residual at slightly different resolutions. As we all know, a single image can be divided into a base layer and a detail layer, and the effective estimation of the detail layer is the key in a detail

Image registration is an essential prerequisite for multisource image fusion. To further improve the registration accuracy for long-wave infrared and visible images, a feature point descriptor construction method—edge-oriented log-Gabor descriptor is proposed. Initially, grayscale edge images are obtained by a structured random forests edge detector, which is more suitable for extracting features using

Riemannian manifold has attracted an increasing amount of attention for visual classification tasks, especially for video or image set classification. Covariance matrices are the natural second-order statistics of image sets. However, nonsingular covariance matrices, known as symmetric positive defined (SPD) matrices, lie on the non-Euclidean Riemannian manifold (SPD manifold). Covariance discriminative

Images collected in fog can lose contrast and fidelity due to the scattering and absorption of light by the fog particles. In particular, unlike common white fog, colored fog exhibits varying degrees of light attenuation at different wavelengths, resulting in the simultaneous deviation of contrast and color. We present the failure of the traditional algorithm in the colored haze removal process. In

Deep convolutional neural networks (CNNs) have achieved considerable success with image denoising. However, they still lack consistent performance across different noise types and levels. We extend noise scenarios to four categories: Gaussian, random-impulse, salt-and-pepper, and Poisson. We also propose a multinoise-type blind denoising network (MBDNet) that solves the blind denoising task using a

Local binary patterns (LBP) are well documented in the literature as descriptors of local image texture, and their histograms have been shown to be well-performing texture features. A method for texture description that is based on the α-cutting approach is presented. The presented approach combines basic definitions from the fuzzy set theory with the main concept of LBP descriptors, which resulted

Road detection is a vital task for autonomous vehicles, as it has a direct link to passengers’ safety. Given its importance, researchers aimed to improve its accuracy and robustness. We look at the task from a holistic point of view, where we aim to balance computation and accuracy. A multimodal road detection pipeline is proposed, which fuses the camera image with the preprocessed LIDAR input. First

Current iris recognition technology faces practical difficulties. For example, the unsteady morphology of a heterogeneous iris is generated by a variety of different devices and environments. The existing iris data sets have difficulty meeting the requirements of learning methods under a single deep learning framework. Research on iris recognition security has limited response to results stealing.

We present a robust approach for detecting defects on an aircraft electrical wiring interconnection system in order to comply with the safety regulations such as the forbidden interference and allowed bend radius of cables and/or harness in mechanical assemblies. For this purpose, we exploit 3-D point clouds acquired with a 3-D scanner and the 3-D computer-aided design (CAD) model of the assembly being

The inspection of solder joints on printed circuit boards is a difficult task because defects inside the joints cannot be observed directly. In addition, because anomalous samples are rarely obtained in a general anomaly detection situation, many methods use only normal samples in the learning phase. However, sometimes a small number of anomalous samples are available for learning. We propose a method

To ease visual inspections of exterior aircraft fuselage, new technical approaches have been recently deployed. Automated unmanned aerial vehicles (UAVs) are now acquiring high-quality images of aircraft in order to perform offline analysis. At first, some acquisitions are annotated by human operators in order to provide a large dataset required to train machine learning methods, especially for critical

A polarization filter array (PFA) camera is an imaging device capable of analyzing the polarization state of light in a snapshot manner. These cameras exhibit spatial variations, i.e., nonuniformity, in their response due to optical imperfections introduced during the nanofabrication process. Calibration is done by computational imaging algorithms to correct the data for radiometric and polarimetric

The purpose of this paper is to explore the use of fully convolutional neural networks (FCN) to perform a semantic segmentation of deflectometric recordings for quality control of reflective surfaces. The proposed method relies on a U-net network to identify the location and boundaries of the object and the possible defective areas present on it by performing a pixel-wise classification based on local

Usage of a three-dimensional (3-D) sensor and point clouds provides various benefits over the usage of a traditional camera for industrial inspection. We focus on the development of a classification solution for industrial inspection purposes using point clouds as an input. The developed approach employs deep learning to classify point clouds, acquired via a 3-D sensor, the final goal being to verify

In recent years, driven by new standards and image brand, product quality has become highly critical in the food industry. Particularly concerning the beverage industry, the potential presence of very small foreign objects, especially glass fragments, has to be checked. The visible optical domain offers an advantageous alternative solution to the expensive and intrusive x-ray systems. However, such

Reflectance transformation imaging (RTI) is a multilight-based imaging technique that can provide relevant information on both local microgeometry and visual appearance of a studied surface. The pixelwise angular reflectance is modeled to allow the relighting of the surface appearance under any arbitrary light direction. The primary methods used to model this local reflectance in each pixel are polynomial

Solutions for the quality control of metallic surfaces are proposed. Specifically, we study a deflectometric apparatus based on coaxial structured light and the related algorithmic procedure, which is able to detect the faulty surface of a sample captured by a video sequence. First, by considering the metallic surface a dynamic scene illuminated under different light conditions, we develop the descriptor

We propose a method to estimate the surface normal of concave objects. The target object of our method has a specular surface without diffuse reflection. We solve the problem by analyzing the polarization state of the reflected light. The polarization analysis gives a constraint to the surface normal. However, polarization data from a single view has an ambiguity and cannot uniquely determine the surface

Burns are one of the obnoxious injuries subjecting thousands to loss of life and physical defacement each year. Both high income and Third World countries face major evaluation challenges including but not limited to inadequate workforce, poor diagnostic facilities, inefficient diagnosis and high operational cost. As such, there is need to develop an automatic machine learning algorithm to noninvasively

Psoriasis is an inflammatory cutaneous disease of unknown origin, characterized by the appearance of red, itchy, and scaly plaques of abnormal skin. One of the most important issues that guides treatment of psoriasis is to evaluate the degree of the illness determining the psoriasis and area severity index (PASI). Dermatologists usually use visual and tactile senses to assess lesion severity, involving

In this work we propose to combine a supervoxel-based image representation with the concept of graph cuts as an efficient optimization technique for 3D deformable image registration. Due to the pixels/voxels-wise graph construction, the use of graph cuts in this context has been mainly limited to 2D applications. However, our work overcomes some of the previous limitations by posing the problem on

Visual prostheses require an effective representation method due to the limited display condition which has only 2 or 3 levels of grayscale in low resolution. Edges derived from abrupt luminance changes in images carry essential information for object recognition. Typical binary (black and white) edge images have been used to represent features to convey essential information. However, in scenes with

High-speed cameras explore more details than normal cameras in the time sequence, while the conventional video sampling suffers from the trade-off between temporal and spatial resolutions due to the sensor's physical limitation. Compressive sensing overcomes this obstacle by combining the sampling and compression procedures together. A single-pixel-based real-time video acquisition is proposed to record

The usual tomography is achieved by acquiring measurements around an object with multiple angles. The possibility of obtaining a fluorescence tomographic image from measurements at only one angle is explored. Instead of rotating around the object, the camera (or the objective lens) moves toward (or away from) the object and takes photographs while the camera's focal plane passes through the object

We investigate the use of different trabecular bone descriptors and advanced machine learning tech niques to complement standard bone mineral density (BMD) measures derived from dual-energy x-ray absorptiometry (DXA) for improving clinical assessment of osteoporotic fracture risk. For this purpose, volumes of interest were extracted from the head, neck, and trochanter of 146 ex vivo proximal femur

The Katsevich algorithm is a breakthrough in the theoretically exact algorithms for helical cone beam CT. For future application in medical and industrial CT, determining how to implement it efficiently and accurately is the main task. We analyzed the slope law and intersection law of the k-lines, finding that the k-lines are not intersecting if the half maximal fan angle (HMFA) is less than 21° (numerical

We discuss improved image reconstruction and segmentation in a framework we term model-controlled flooding (MCF). This extends the watershed transform for segmentation by allowing the integration of a priori information about image objects into flooding simulation processes. Modeling the initial seeding, region growing, and stopping rules of the watershed flooding process allows users to customize

In this paper we describe and evaluate a fast implementation of a classical block matching motion estimation algorithm for multiple Graphical Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) computing engine. The implemented block matching algorithm (BMA) uses summed absolute difference (SAD) error criterion and full grid search (FS) for finding optimal block displacement

The authors developed an integrated computer-aided detection (CAD) scheme for detecting and classifying metaphase chromosomes as well as assessing its performance and robustness. This scheme includes an automatic metaphase-finding module and a karyotyping module and it was applied to a testing database with 200 digital microscopic images. The automatic metaphase-finding module detects analyzable metaphase