Steganography is a technique applied to ensure secure communication. It is challenged by visual observation or statistical analysis to ascertain whether a message is hidden within a cover medium. Several steganographic methods have been proposed for palette-based images. These methods maintain image quality but cannot resist some statistical and visual attacks. To overcome this problem, two parity

It is very challenging to recognize the information of occluded Tibetan document pages due to the lack of digitization and their long-term storage. Multiple pages are stuck, and textual characters are occluded with each other, which causes difficulties in restoration. Due to the large size of Tibetan documents, it is impossible to separate and repair these occluded pages by professionals. Therefore

Semantic segmentation of three-dimensional (3D) scenes is a challenging task in 3D scene understanding. Recently, deep learning-based segmentation approaches have made significant progress. A multiscale spatial context feature learning is used in an end-to-end approach for 3D point cloud semantic segmentation. Furthermore, a local feature fusion learning block is then introduced to the hidden layers

Gender classification has found many useful applications in the broader domain of computer vision systems including in-cabin driver monitoring systems, human–computer interaction, video surveillance systems, crowd monitoring, data collection systems for the retail sector, and psychological analysis. In previous studies, researchers have established a gender classification system using visible spectrum

Rain streaks attached to a camera may seriously affect the visibility of the background and considerably degrade image quality. We handle this problem by removing rain streaks to convert a rainy image into a clean one. This creates a problem in that the information with respect to the background of the occluded parts is close to being lost for the most part. To remove rain streaks from a single image

Infrared-visible cross-modality person re-identification (IV-ReID) is a challenging task that aims to match infrared person images with visible person images of same identity. The person images of two modalities are captured by visible cameras and infrared cameras, respectively. Due to the variation between two modalities, most existing methods tend to extract common features of different modalities

Object tracking is challenging due to unconstrained variations in the target’s appearance and complex environmental variations. Appearance models based on a single cue are inefficient in addressing the various tracking challenges. To address this, we propose a discriminative visual tracking approach in which complementary multicue features viz. RGB cue and histogram of gradient are integrated to build

Blind deconvolution is known as a challenging low-level vision problem due to the diverse blur scenarios in real-world imaging. Another attempt is made with critical thoughts on existing image priors for nonparametric blur kernel estimation, proposing an alternative approach to blind deconvolution via complementarily structure-aware image smoothing (CSIS). Similar to most state-of-the-art blind deblurring

The fusion of depth acquired actively with the depth estimated passively proved its significance as an improvement strategy for gaining depth. This combination allows us to benefit from two sources of modalities such that they complement each other. To fuse two sensor data into a more accurate depth map, we must consider the limitations of active sensing such as low lateral resolution while combining

It is a challenge to remove the cluttered background in research of hand gesture images. The popular method, image semantic segmentation, is still not efficient enough to deal well with fine-grained image background removal due to insufficient training samples. We are the first to propose a background removal method based on a conditional generative adversarial network (CGAN). With CGAN, our method

Current self-supervised monocular methods only learn effectively by imposing consistency constraints without relying on any geometric constraints or ground truth depth constraints, which makes the accuracy of the estimation result suboptimal. Compared with the monocular algorithm, the stereo matching network usually follows the geometric process of the traditional stereo algorithm, which makes the

We describe the improvements of the content-based image retrieval (CBIR) system using a fuzzy class membership for the natural-color images. The fuzzy class membership-based retrieval (CMR) framework has shown promising improvements on texture databases by exploiting confidence in classification using a multilayer perceptron (MLP). CMR is known to improve the average precision of retrieval along with

Tracking objects with the scale variations quickly is a challenging problem in visual tracking. Most existing methods estimate the scale of the object with an exhaustive search strategy, which needs large calculations but with less improvement. We propose to use twofold weighted phase correlation with the kernel (WPCK) to simultaneously estimate the translation and scale for the object during tracking

A motion energy image (MEI) is a spatial template that collapses regions of motion into a single image in which more moving pixels are brighter than others. The forward single-step history image (fSHI) is a spatiotemporal template that shows the presence and direction of motion. Each video can be described using these templates. Recently, the popularity of deep learning architectures for human activity

Biometric identification from three-dimensional (3-D) face surface characteristics has become popular. Traditional face recognition methods have achieved very high recognition accuracy under controlled environments. However, 3-D face recognition technology still faces a great challenge for facial expressions and missing data caused by pose variations or occlusions. The mesh-based scale-invariant feature

Development of computer vision technologies has been widely used to increase the level of agricultural intelligence. Crop counting, an application of image counting, plays a fundamental role in agricultural information automation. However, the complex cotton field environment is likely lead to incorrect detection of the target position or fragmentation of the segmentation results, resulting in a decrease

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

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.

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

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