Fusion in Dissimilarity Space Between RGB D and Skeleton for Person Re Identification

Person re-identification (Re-id) is one of the important tools of video surveillance systems, which aims to recognize an individual across the multiple disjoint sensors of a camera network. Despite the recent advances on RGB camera-based person re-identification methods under normal lighting conditions, Re-id researchers fail to take advantages of modern RGB-D sensor-based additional information (e.g. depth and skeleton information). When traditional RGB-based cameras fail to capture the video under poor illumination conditions, RGB-D sensor-based additional information can be advantageous to tackle these constraints. This work takes depth images and skeleton joint points as additional information along with RGB appearance cues and proposes a person re-identification method. We combine 4-channel RGB-D image features with skeleton information using score-level fusion strategy in dissimilarity space to increase re-identification accuracy. Moreover, our propose method overcomes the illumination problem because we use illumination invariant depth image and skeleton information. We carried out rigorous experiments on two publicly available RGBD-ID re-identification datasets and proved the use of combined features of 4-channel RGB-D images and skeleton information boost up the rank 1 recognition accuracy.

Underwater image enhancement method with non-uniform illumination based on Retinex and ADMM

In order to solve the image blurring and distortion problem caused by underwater non-uniform and low illumination, this paper proposes an underwater image enhancement algorithm based on the Retinex theory and the Alternating Direction Method of Multipliers (ADMM). Firstly, the L component of the original image in the Lab space is extracted as the initial illumination map, and an Augmented Lagrange Multiplier (ALM) framework is constructed based on the ADMM to optimize the initial illumination map in order to obtain an accurate illumination image. In addition, the illumination map is further corrected in the luminance region with the Gamma Correction. Secondly, combined with the color constancy characteristics in the Retinex theory, the reflected image of the object is obtained. Finally, the bilateral filter is picked to suppress the underwater noise and obtain a more detailed enhanced image. The experimental results show that the underwater image enhancement algorithm can effectively solve the non-uniform illumination problem caused by natural light or artificial light source and improve the underwater image quality, thus having a better performance than other algorithms.

Image Enhancement Algorithm Based on Depth Difference and Illumination Adjustment

In order to improve the clarity and color fidelity of traffic images under the complex environment of haze and uneven illumination and promote road traffic safety monitoring, a traffic image enhancement model based on illumination adjustment and depth of field difference is proposed. The algorithm is based on Retinex theory, uses dark channel principle to obtain image depth of the field, and uses spectral clustering algorithm to cluster image depth. After the subimages are divided, the local haze concentration is estimated according to the depth of field and the subimages are adaptively enhanced and fused. In addition, the illumination component is obtained by multiscale guided filtering to maintain the edge characteristics of the image, and the uneven illumination problem is solved by adjusting the curve function. The experimental results show that the proposed model can effectively enhance the uneven illumination and haze weather image in the traffic scene and the visual effect of the images is good. The generated image has rich details, improves the quality of traffic images, and can meet the needs of traffic practical application.

Software Library For The Lighting Fixtures Optical characteristics Calculation By The Computer-aided design System Kompas 3D

The advantages and disadvantages of existing solutions for calculating lighting fixtures (LF) are discussed in the paper. The methods for solving the global illumination problem are demonstrated. The most important illumination models used to calculate LFs have been analyzed. The Torrance-Sparrow model is used as the primary model of reflective surface. The triangulation method based on approximation of the LF surface by a grid of triangles is used to increase the efficiency of LF design and computation. The optical ray propagation in a 3D optical system was modelled using classical laws of optics and the Monte Carlo method. The structure and the main steps of working with the library for CAD Kompas are described. The approximation of LF surface for different types of light sources and reflector parameters is presented. The implementation of approximations with subsequent ray tracing has demonstrated a good convergence of the problem solution. The results of the study have the potential to be useful for design engineers involved in the design and calculation of LFs, as well as developers of applied software in the field of computer graphics and computer-aided design systems.

Quantitative laryngoscopy with computer-aided diagnostic system for laryngeal lesions

Laryngoscopes are widely used in the clinical diagnosis of laryngeal lesions, but such diagnosis relies heavily on the physician's subjective experience. The purpose of this study was to develop a computer-aided diagnostic system for the detection of laryngeal lesions based on objective criteria. This study used the distinct features of the image contour to find the clearest image in the laryngoscopic video. First to reduce the illumination problem caused by the laryngoscope lens, which could not fix the position of the light source, this study proposed image compensation to provide the image with a consistent brightness range for better performance. Second, we also proposed a method to automatically screen clear images from laryngoscopic film. Third, we used ACM to segment automatically them based on structural features of the pharynx and larynx, using hue and geometric analysis in the vocal cords and other zones. Finally, the support vector machine was used to classify laryngeal lesions based on a decision tree. This study evaluated the performance of the proposed system by assessing the laryngeal images of 284 patients. The accuracy of the detection for vocal cord polyps, cysts, leukoplakia, tumors, and healthy vocal cords were 93.15%, 95.16%, 100%, 96.42%, and 100%, respectively. The cross-validation accuracy for the five classes were 93.1%, 94.95%, 99.4%, 96.01% and 100%, respectively, and the average test accuracy for the laryngeal lesions was 93.33%. Our results showed that it was feasible to take the hue and geometric features of the larynx as signs to identify laryngeal lesions and that they could effectively assist physicians in diagnosing laryngeal lesions.

Fast VMZ : code enhancements for video mosaicing and summarization

Video Mosaicing and Summarization (VMZ) is a novel image processing pipeline that summarizes the content of a long sequence of geospatial or biomedical videos using a few coverage maps or mini mosaics. The existing VMZ algorithm uses Normalized Cross-Correlation (NCC), Structure Tensor (ST), Affine-Invariant SIFT (ASIFT), Speeded up robust features for its feature matching and homography estimation pipeline, which are the most computationally expensive modules in the VMZ pipeline. Due to these long-running compute-intensive modules, the VMZ pipeline is not suitable for real-time mosaic formation in drones or UAVs. For instance, VMZ takes around 4 hours to generate mini-mosaics from an image sequence containing 9291 image frames. The blending algorithms used for mini-mosaic generation suffer from illumination variation due to the illumination difference in image frames. Such illumination inconsistency causes severe problems for biomedical scene understanding where curvilinear or tiny biological structures are present. VMZ pipeline is also dependent on 3rd party libraries not aligned with the flow of VMZ, which introduces redundant computation. One of the main reasons for the slow processing of the VMZ pipeline is not leveraging any parallel processing techniques and available graphics processing hardware. Therefore, the objective of this thesis is mainly three-fold: (i) speeding up the computeintensive and long-running modules in the VMZ pipeline, (ii) modifying the existing libraries and interfaces for better alignment with VMZ workflow, and (iii) resolving the illumination difference problem of the blending algorithms. Selected longrunning modules with the most impact on the overall run-time have been improved using CPU-based Multi-Threading, GPU-based Parallelization, and better integration with the existing VMZ pipeline. An illumination-matched blending algorithm has been proposed to improve the illumination problem. Besides, to evaluate the performance of different blending algorithms, a novel metric named Maximum Overall Illumination Difference (MOID) has been proposed. The improvement of VMZ modules has resulted in more than 100x speed-up in certain modules, with a 4.4x speed-up for the total VMZ run-time. The novel illumination matched blending resulted in a better MOID value for image sequences not having illumination variance in a single frame.

Quantitative Laryngoscopy With Computer-aided Diagnostic System for Laryngeal Lesions

Background: Laryngoscopes are widely used in the clinical diagnosis of laryngeal lesions, but such diagnosis relies heavily on the physician's subjective experience. The purpose of this study was to develop a computer-aided diagnostic system for the detection of laryngeal lesions based on objective criteria.Methods: This study used the distinct features of the image contour to find the clearest image in the laryngoscopic video. For the illumination problem caused by the laryngoscope lens, which could not fix the position of the light source, this study proposed image compensation to provide the image with a consistent brightness range. We also proposed a method to automatically screen clear images from laryngoscopic film and automatically segment them based on structural features of the pharynx and larynx, using hue and geometric analysis in the vocal cords and other zones. Finally, the support vector machine was used to classify laryngeal lesions based on a decision tree.Results: This study evaluated the performance of the proposed system by assessing the laryngeal images of 284 patients. The accuracy of the detection for vocal cord polyps, cysts, leukoplakia, tumors, and healthy vocal cords were 93.15%, 95.16%, 100%, 96.42%, and 100%, respectively, and the classification accuracy for laryngeal lesions was 96.47%.Conclusion: Our results showed that it was feasible to take the hue and geometric features of the larynx as signs to identify laryngeal lesions and that they could effectively assist physicians in diagnosing laryngeal lesions.

An Automated Framework for Patient Identification and Verification Using Deep Learning

Automated patient identification and verification are very important at a medical emergency and when patients are not carrying his/her identity. It is a risk factor that identifying the correct patient identity for doctors to provide medical treatment. The majority of the identification or verification is being done by wristbands, RFID tags, fingerprint, face detection by using handcraft feature-based face recognition systems. A new framework based on robust deep learning model and contrast enhancement is proposed in this paper. In the proposed work, the light illumination problem has been addressed by the contrast enhancement technique for deep learning models to recognize the face. It is proved that the inclusion of contrast enhancement is improving patient identification and verification. To evaluate the deep learning framework, the proposed deep learning models have been trained on our own dataset and have been tested with a real-time medical providing agency. The experimental results show that the proposed framework exhibits more robust test results with accuracy than existing hand-crafted techniques under the live webcam video capture for the real-time patient detection system.

Identifying the Faces from Poor Quality Image / Video

Face biometric is becoming more popular because of its wide range of applications in authorizing the person either from an image or from the video sequence. The bottleneck in face recognition is Pose angle variation, varying light condition, Partial Occlusion, Blur in the image or Noise. The proposed method first removes the noise from the image using Adaptive Median Filter (AMF) then Discrete Cosine Transform(DCT) is applied to normalize the illumination problem. The algorithm is further used to remove the motion blur using Lucy Richardson’s method by calculating the Point Spread Function (PSF). The Pose variation problem is then addressed with Global Linear Regression(GLR). Then the Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA) are applied to the normalized image to get the feature vector. This combined feature score is used to recognize the image using K-Nearest Neighbor (K-NN). The result shows a maximum accuracy of 92% and 87.5% with Pose angle variation between (0°, 22°) and (22°, 40°) respectively. The pose variation greater than this shows an average accuracy of 77.5%. The result also shows a maximum computation speed of 0.018 Seconds.

Illumination Invariant Face Recognition

Illumination variation is one of the well-known problems in face recognition, especially in uncontrolled environments. This chapter presents an extensive and up-to-date survey of the existing techniques to address this problem. This survey covers the conventional passive techniques that attempt to solve the illumination problem by studying the visible light images, in which face appearance has been altered by varying illumination, as well as the active techniques that aim to obtain images of face modalities invariant to environmental illumination.