Illumination Invariant Features Research Articles

Research on trajectory control of multi‐degree‐of‐freedom industrial robot based on visual image

AbstractIn order to improve the trajectory control effect of multi‐degree‐of‐freedom industrial robots, this paper combines visual image technology to conduct research on trajectory control of multi‐degree‐of‐freedom industrial robots. Aiming at the problem of video segmentation under sudden illumination changes, this paper uses a Gaussian mixture model based on the global illumination function to adopt a variety of illumination invariant features, and proposes a scene segmentation algorithm suitable for sudden illumination changes. Moreover, this paper compares and verifies the algorithm from the subjective and objective perspectives through experiments, which shows that the algorithm in this paper can segment the scene more accurately even in the environment of sudden changes in illumination. In addition, the results of the accuracy test and the trajectory control test show that the research method of the multi‐degree‐of‐freedom industrial robot trajectory control based on the visual image proposed in this paper can effectively improve the trajectory control effect of the robot.

Statement of Removal

ABSTRACT Aiming at the problem that uneven illumination easily affects the facial expression recognition rate and reduces the facial expression recognition rate, a facial expression recognition algorithm based on local features and deep belief network (DBN) is proposed. Firstly, the non-uniform illumination invariant feature of LSH in facial expression image is extracted; Secondly, Gauss Laplace operator (log) is used to extract the edge detail features of facial expression; Then, the facial expression features fused by LSH and log are constructed; Finally, the DBN model is used to train the facial expression features fused by LSH and log, and the trained DBN model is used for facial expression recognition. Experiments on Jaffe facial expression database and Uighur facial expression database show that the algorithm has strong robustness and effectiveness.

Classification of Gastric Lesions Using Gabor Block Local Binary Patterns

The identification of cancer tissues in Gastroenterology imaging poses novel challenges to the computer vision community in designing generic decision support systems. This generic nature demands the image descriptors to be invariant to illumination gradients, scaling, homogeneous illumination, and rotation. In this article, we devise a novel feature extraction methodology, which explores the effectiveness of Gabor filters coupled with Block Local Binary Patterns in designing such descriptors. We effectively exploit the illumination invariance properties of Block Local Binary Patterns and the inherent capability of convolutional neural networks to construct novel rotation, scale and illumination invariant features. The invariance characteristics of the proposed Gabor Block Local Binary Patterns (GBLBP) are demonstrated using a publicly available texture dataset. We use the proposed feature extraction methodology to extract texture features from Chromoendoscopy (CH) images for the classification of cancer lesions. The proposed feature set is later used in conjuncture with convolutional neural networks to classify the CH images. The proposed convolutional neural network is a shallow network comprising of fewer parameters in contrast to other state-of-the-art networks exhibiting millions of parameters required for effective training. The obtained results reveal that the proposed GBLBP performs favorably to several other state-of-the-art methods including both hand crafted and convolutional neural networks-based features.

Holistic Feature Reconstruction-Based 3-D Attention Mechanism for Cross-Spectral Periocular Recognition

Recognition of individuals in cross-spectral environments involves situations where probe and gallery images are captured in distinct wavelength ranges and hence differ significantly in terms of appearance and illumination. In case of heterogeneous periocular images, alleviating this wide appearance gap and learning to extract illumination-invariant features from such local regions become cumbersome, giving rise to a challenging research problem. In this work, we design a novel holistic feature reconstruction-based attention module (H-FRAM) to refine and generate discriminative convolutional features. In contrast to existing spatial and channel attention mechanisms that compute 2-D or 1-D attention weights respectively, H-FRAM calculates the importance of each feature location by performing multi-linear principal component analysis on full 3-D tensor space. In H-FRAM, we claim that the feature reconstruction error, computed in a holistic manner, plays a crucial role in determining the relevance of feature locations. This reconstruction error is found by projecting the input feature map onto a multi-dimensional eigenspace that captures most of the important variations. To our knowledge, this is the first work which explores subspace learning approach in the context of 3-D attention mechanism to capture discriminative feature information. We have created an in-house cross-spectral periocular dataset containing visible and near-infrared images from 200 classes. The images are captured in unconstrained acquisition setup involving unsupervised eye and head movements as well as accessory variations (face masks and eyeglasses). Extensive experiments and ablation studies show that the proposed network achieves state-of-the-art recognition performances on the existing and in-house datasets for heterogeneous and homogeneous periocular recognition as well as heterogeneous face recognition.

A Novel Zernike Moment-Based Real-Time Head Pose and Gaze Estimation Framework for Accuracy-Sensitive Applications.

A real-time head pose and gaze estimation (HPGE) algorithm has excellent potential for technological advancements either in human-machine or human-robot interactions. For example, in high-accuracy advent applications such as Driver's Assistance System (DAS), HPGE plays a crucial role in omitting accidents and road hazards. In this paper, the authors propose a new hybrid framework for improved estimation by combining both the appearance and geometric-based conventional methods to extract local and global features. Therefore, the Zernike moments algorithm has been prominent in extracting rotation, scale, and illumination invariant features. Later, conventional discriminant algorithms were used to classify the head poses and gaze direction. Furthermore, the experiments were performed on standard datasets and real-time images to analyze the accuracy of the proposed algorithm. As a result, the proposed framework has immediately estimated the range of direction changes under different illumination conditions. We obtained an accuracy of ~85%; the average response time was 21.52 and 7.483 ms for estimating head poses and gaze, respectively, independent of illumination, background, and occlusion. The proposed method is promising for future developments of a robust system that is invariant even to blurring conditions and thus reaching much more significant performance enhancement.

Study on Reflection-Based Imaging Finger Vein Recognition

Finger vein modality plays an important role in biometrics due to its stability and security. However, existing state-of-the-art finger vein recognition systems adopt the transmission-based imaging mode with a sealed design, which requires redundant space in the imaging device and results in an uncomfortable user experience. Consequently, we design a reflection-based imaging device with an open structure to reduce the device volume and improve the portability, as well as the user experience. However, an open structure of the device inevitably introduces extra illumination variation to the image, which may deteriorate the performance of the system. In this paper, we propose Domain Adaptation Finger Vein Network (DAFVN) to narrow the domain shift between different illumination data domains and extract illumination-invariant features from finger vein images, improving the robustness to illumination variations. To evaluate the performance of DAFVN and remedy the lack of a publicly open reflection-based finger vein database, we use the self-made device to construct the first large-scale reflection-based finger vein database, namely SCUT Reflective Imaging Finger Vein database (SCUT-RIFV). It includes 32,064 images from 167 subjects with five different illumination conditions. Abundant experiments implemented on the SCUT-RIFV database indicate that the proposed method can effectively alleviate the influence of illumination variation on the reflection-based finger vein recognition system.

LISU: Low-light indoor scene understanding with joint learning of reflectance restoration

Semantic segmentation using convolutional neural networks (CNNs) achieves higher accuracy than traditional methods, but it fails to yield satisfactory results under illumination variants when the training set is limited. In this paper we present a new data set containing both real and rendered images and a novel cascade network to study semantic segmentation in low-light indoor environments. Specifically, the network decomposes a low-light image into illumination and reflectance components, and then a multi-tasking learning scheme is built. One branch learns to reduce noise and restore information on the reflectance (reflectance restoration branch). Another branch learns to segment the reflectance map (semantic segmentation branch). The CNN features from two tasks are concatenated together so as to improve the segmentation accuracy by embedding the illumination-invariant features. We compare our approach with other CNN-based segmentation frameworks, including the state-of-the-art DeepLab v3+, on the proposed real data set, and our approach achieves the highest mIoU (47.6%). The experimental results also show that the semantic information supports the restoration of a sharper reflectance map, thus further improving the segmentation. Besides, we pre-train a model with the proposed large-scale rendered images and then fine-tune it on the real images. The pre-training results in an improvement of mIoU by 7.2%. Our models and data set are publicly available for research. This research is part of the EU project INGENIOUS11https://ingenious-first-responders.eu/. Our data sets and models are available on our website22https://github.com/noahzn/LISU.

Development of scale and illumination invariant feature detector with application to UAV attitude estimation

Feature detection has great importance in many applications such as vision navigation. Examining the developed detectors, it is found in many recent studies that most of the scale-invariant detectors are sensitive to illumination. In this work, we propose a novel detector that has good robustness to both scale and illumination. Motivated by the good robustness of Log-Gabor kernels toward light changes, we employ these kernels as a basis to construct the scale space. To detect potential features, we develop an effective interest points measure which is motivated by the concept of the autocorrelation and Hessian matrices. To confirm the good performance of our detector, we hold experiments on many datasets and with comparisons to common state-of-the-art methods. Furthermore, we evaluate the saliency of the detected features on a UAV attitude estimation task.

TOWARDS LEARNING LOW-LIGHT INDOOR SEMANTIC SEGMENTATION WITH ILLUMINATION-INVARIANT FEATURES

Abstract. Semantic segmentation models are often affected by illumination changes, and fail to predict correct labels. Although there has been a lot of research on indoor semantic segmentation, it has not been studied in low-light environments. In this paper we propose a new framework, LISU, for Low-light Indoor Scene Understanding. We first decompose the low-light images into reflectance and illumination components, and then jointly learn reflectance restoration and semantic segmentation. To train and evaluate the proposed framework, we propose a new data set, namely LLRGBD, which consists of a large synthetic low-light indoor data set (LLRGBD-synthetic) and a small real data set (LLRGBD-real). The experimental results show that the illumination-invariant features effectively improve the performance of semantic segmentation. Compared with the baseline model, the mIoU of the proposed LISU framework has increased by 11.5%. In addition, pre-training on our synthetic data set increases the mIoU by 7.2%. Our data sets and models are available on our project website.

A New Face Image Recognition Algorithm Based on Cerebellum-Basal Ganglia Mechanism.

Face recognition is one of the popular areas of research in the field of computer vision. It is mainly used for identification and security system. One of the major challenges in face recognition is identification under numerous illumination environments by changing the direction of light or modifying the lighting magnitude. Exacting illumination invariant features is an effective approach to solve this problem. Conventional face recognition algorithms based on nonsubsampled contourlet transform (NSCT) and bionic mode are not capable enough to recognize the similar faces with great accuracy. Hence, in this paper, an attempt is made to propose an enhanced cerebellum-basal ganglia mechanism (CBGM) for face recognition. The integral projection and geometric feature assortment method are used to acquire the facial image features. The cognition model is deployed which is based on the cerebellum-basal ganglia mechanism and is applied for extraction of features from the face image to achieve greater accuracy for recognition of face images. The experimental results reveal that the enhanced CBGM algorithm can effectively recognize face images with greater accuracy. The recognition rate of 100 AR face images has been found to be 96.9%. The high recognition accuracy rate has been achieved by the proposed CBGM technique.

Pose and Illumination Invariant Hybrid Feature Extraction for Newborn

Background: Abduction, swapping and mix-ups are the unfortunate events that could happen to Newborn while in hospital premises and medical personnel are finding it difficult to curb this unfortunate incident. Accurate patient identification (ID) is essential for patient safety, especially with our smallest and most vulnerable paediatric patients. The level of security is very crucial issue in maternity ward and the problem of missing and swapping of Newborn is of prime concern to the persons involved and affected. There is a common perception in the society that nothing can be done to prevent this unfortunate tragedy. In comparison to developed nations the developing countries are facing more challenges because of overcrowding and scarcity of medical facilities in the hospital. The face of a newborn baby changes appearance in different lighting conditions in spite of various contrast enhancement techniques. The class of images which are under similar environment conditions (inter-class) may result in more differences than that of the intra-class images under different illumination conditions. This gives rise to the misclassification of images. Objective: The main objective of this paper is perform newborn face recognition in a hybrid approach. Speeded Up Robust Features (SURF) and Local Binary Pattern (LBP). Methods: The scientific contributions of the proposed work are stated below: For face recognition of newborns in a semi controlled environment. Overcoming the pose and illumination challenge. Uses single gallery image. Uses a hybrid approach to improve the results. Conclusion: In this paper new semi supervised technique is used for demonstrate the improve the performance of the newborn face recognition system in different illumination and pose conditions. In our. Results: The average Rank 1 accuracy of the proposed method is 93.65% whereas that of the existing algorithms is quite low. The proposed technique is 12.8% more accurate than LBP, 10.5% more accurate than SURF, 12.8% more accurate than LDA and 18.1% more accurate than PCA for rank1.

Study on intelligent traffic search method based on driver facial feature analysis

With the rapid development of Internet technology and biometrics technology in China, artificial intelligence has gradually entered into every aspect of people's life. Big data is used to upgrade intelligent traffic search for people, which improves the efficiency and accuracy of people search for people. Intelligent traffic search is a hotspot in the field of biometric identification and plays an important role in social stability. As an important feature, driver's face image can not only provide great help to the detection of illegal vehicles, but also help to carry out the tracing of missing people, so as to maintain social harmony and stability. Therefore, the intelligent traffic search method based on the analysis of driver's facial features has a broad application prospect and research value. This paper investigates the current international top facial recognition algorithm technology level, and proposes a face image illumination invariant feature extraction algorithm and face feature detection ASM algorithm. The experimental results show that the intelligent traffic search method in this paper has a good recognition rate, and the study in this paper also has a certain guiding significance for the application of image processing in the field of intelligent traffic.

Study on intelligent traffic search method based on driver facial feature analysis

With the rapid development of Internet technology and biometrics technology in China, artificial intelligence has gradually entered into every aspect of people's life. Big data is used to upgrade intelligent traffic search for people, which improves the efficiency and accuracy of people search for people. Intelligent traffic search is a hotspot in the field of biometric identification and plays an important role in social stability. As an important feature, driver's face image can not only provide great help to the detection of illegal vehicles, but also help to carry out the tracing of missing people, so as to maintain social harmony and stability. Therefore, the intelligent traffic search method based on the analysis of driver's facial features has a broad application prospect and research value. This paper investigates the current international top facial recognition algorithm technology level, and proposes a face image illumination invariant feature extraction algorithm and face feature detection ASM algorithm. The experimental results show that the intelligent traffic search method in this paper has a good recognition rate, and the study in this paper also has a certain guiding significance for the application of image processing in the field of intelligent traffic.

Illumination-Invariant Feature Point Detection Based on Neighborhood Information.

Feature point detection is the basis of computer vision, and the detection methods with geometric invariance and illumination invariance are the key and difficult problem in the field of feature detection. This paper proposes an illumination-invariant feature point detection method based on neighborhood information. The method can be summarized into two steps. Firstly, the feature points are divided into eight types according to the number of connected neighbors. Secondly, each type of feature points is classified again according to the position distribution of neighboring pixels. The theoretical deduction proves that the proposed method has lower computational complexity than other methods. The experimental results indicate that, when the photometric variation of the two images is very large, the feature-based detection methods are usually inferior, while the learning-based detection methods performs better. However, our method performs better than the learning-based detection method in terms of the number of feature points, the number of matching points, and the repeatability rate stability. The experimental results demonstrate that the proposed method has the best illumination robustness among state-of-the-art feature detection methods.

Description method of Illumination invariant image features

Image feature extraction technology is one of main topics in the field of computer vision, which has been widely applied in biological recognition, image retrieval, target detection and other fields. To overcome the drawbacks of WLD under complex illumination condition, we propose a novel illumination–insensitive feature descriptor named as anisotropic Weber synergy gradient descriptor (AWSGD). The proposed algorithm contains two parts:differential excitation component and gradient direction component. Firstly, by introducing the differential synergy excitation pattern (DSEP) and anisotropic LOG operator with variable scales and angles, we propose the anisotropic differential synergy excitation pattern (ADSEP) as the differential excitation component. Next, focused on the shortage that local gradient pattern (LGP) lacks detailed description of local features with single-layer model, we propose weighted local synergy gradient pattern (WLSGP) as the gradient direction component based on two-layer structure model and weight coefficient distribution model. Finally, ADSEP and WLSGP are fused to form AWSGD histogram. Meanwhile, we adopt XGBoost classifier to conduct related experiments on face databases CMUPIE, Yale B and texture databases PhoTex, RawFooT. The experimental results indicate that the proposed algorithm has stronger robustness to illumination variation and achieves the best performance compared with state-of-the-art methods, which has a certain theoretical significance and practical value in image recognition field under complex illumination condition.

Illumination and Rotation Invariant Texture Representation for Face Recognition

This article presents a novel approach for illumination and rotation invariant texture representation for face recognition. A gradient transformation is used as illumination invariance property and a Galois Field for the rotation invariance property. The normalized cumulative histogram bin values of the Gradient Galois Field transformed image represent the illumination and rotation invariant texture features. These features are further used as face descriptors. Experimentations are performed on FERET and extended Cohn Kanade databases. The results show that the proposed method is better as compared to Rotation Invariant Local Binary Pattern, Log-polar transform and Sorted Local Gradient Pattern and is illumination and rotation invariant.

Fusing pyramid histogram of gradients and optical flow for hand gesture recognition

Human computer interaction systems based on hand gestures catch the eye of the research community for implementing natural communication between man and machines. However, different persons perform the same gestures differently in terms of velocity and motion scale. This poses a challenging issue in minimising the variations between different persons and maximises the coherence of the same gestures. In this paper, the original pyramid histogram of gradients in three orthogonal planes combining with the dense optical flow to create dynamic descriptor is explored in to discriminate features for recognition of hand gestures. The shape and motion features of images in a video sequence are captured to obtain the geometric and illumination invariant spatio-temporal feature descriptor for classification. A multiclass support vector machine classifier is used to recognise the hand gestures. The proposed method gives an excellent recognition rate and excels the existing approaches.

Moving Cast Shadows Segmentation Using Illumination Invariant Feature

This paper presents an effective framework for removing moving cast shadows. Taking the reflection property of object surface for shadow regions under static and fixed scenes, an approximation estimation strategy of bidirectional reflectance distribution function as illumination invariant feature is proposed. It is valid for different types of shadow scenes. In this paper, we propose a new multiple ratios-based technique to justify shadow type for each frame: intensity ratio, area ratio and edge ratio of shadow regions are introduced. According to shadow types, several specified strategies are designed. For weak shadows, multiple features fusion strategy is employed, including color constancy, texture consistency and illumination invariant. For strong shadows, illumination invariant is utilized to detect the umbra and color constancy is utilized to detect the penumbra. Moreover, a suite of shadow direction features is firstly proposed to identify penumbra. The proposed approach is verified in fourteen video sequences varying from weak to strong shadows. The experimental results demonstrate the effectiveness and robustness of the proposed method for both indoor and outdoor scenes compared with some state-of-the-art approaches.

Research on preprocessing algorithm based on complex illumination condition

In the field of image processing, complex illumination conditions have a serious impact on the accurate recognition of targets. Pre-processing complex illumination before face recognition can improve the accuracy of face recognition. In this paper, the Light is preprocessed by image enhancement algorithm and illumination invariant feature extraction algorithm based on Lambertian illumination model. The image enhancement algorithm achieves the purpose of preprocessing by adjusting the gray scale, and the illumination invariant feature algorithm achieves the purpose of preprocessing by extracting the features of the face. Finally, the advantages and disadvantages of the two types of algorithms are compared and analyzed through experimental results.

Facial gender recognition using Gabor-DCT feature extraction

Facial Gender Identification has vast application in human computer interaction, determines customer profile in shopping centers, and restricted permission to enter in prohibited zone, criminal profile analysis. This paper presents a robust process for illumination invariant compact feature extraction using Gabor filter for the automatic recognition for facial gender identification system. Face has uniqueness in edges and texture pattern for different gender category. Gabor filter can extract edge and textural patterns of faces but generate problem of huge dimensions and redundancy feature coefficients. In order to enhance the efficiency and accuracy of the system, this problem of enormous redundancy as well as dimension can be solved by proposing a new feature namely average-DCT feature reduction technique. Proposed Gabor-DCT has precise, accurate and compact feature pattern as well as early throughput for facial gender identification compared to other state of art method of Gabor filter.