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Analyzes differential flow constraints commonly used to compute optical flow and its relation with underlying intensity patterns. Motion and optical flow; Methods in the calculation of optical flows; Mapping flow constraints to the image brightness structure; Tracking problems; Effects of image density distribution.

Human-Object Interaction (HOI) detection recognizes how persons interact with objects, which is advantageous in autonomous systems such as self-driving vehicles and collaborative robots. However, current HOI detectors are often plagued by model inefficiency and unreliability when making a prediction, which consequently limits its potential for real-world scenarios. In this paper, we address these challenges by proposing ERNet, an end-to-end trainable convolutional-transformer network for HOI detection. The proposed model employs an efficient multi-scale deformable attention to effectively capture vital HOI features. We also put forward a novel detection attention module to adaptively generate semantically rich instance and interaction tokens. These tokens undergo pre-emptive detections to produce initial region and vector proposals that also serve as queries which enhances the feature refinement process in the transformer decoders. Several impactful enhancements are also applied to improve the HOI representation learning. Additionally, we utilize a predictive uncertainty estimation framework in the instance and interaction classification heads to quantify the uncertainty behind each prediction. By doing so, we can accurately and reliably predict HOIs even under challenging scenarios. Experiment results on the HICO-Det, V-COCO, and HOI-A datasets demonstrate that the proposed model achieves state-of-the-art performance in detection accuracy and training efficiency. Codes are publicly available at https://github.com/Monash-CyPhi-AI-Research-Lab/ernet. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for Human Face Recognition, namely Regularized Bi-partitioned Entropy Component Analysis (RBECA). This conservative approach regularizes the kernel entropy components by deterring the noise and affecting the lower entropy regions area, making the method robust to noise. The kernel feature space, formed by the kernel entropy component analysis (KECA), is divided into two partitions: the High Entropy Space (HES) and the Low Entropy Space (LES). The noise-laden low entropy spectrum is regularized by predicting entropy values obtained from the information-filled High Entropy Spectrum. The corresponding projection vectors are adjusted accordingly. A null space, comprising the negligible information and many dimensions, is eliminated using a Golden Search minimization function at two stages. The method retains the maximum entropy property and high recognition accuracy while using the optimum number of features. This resultant feature vector is classified using the cosine similarity measure. The algorithm is successfully tested on several benchmark databases like AR, FERET, FRAV2D, and LFW, using standard protocols and compared with other competitive methods. The proposed method achieves much better recognition accuracy than other well-known methods like PCA, ICA, KPCA, KECA, LGBP, ERE, etc., in all considered cases. Moreover, we have also proposed a CNN for the comparative analysis. For unbiased or fair performance evaluation, the sensitivity and specificity are also reported. • A noise robust RBECA method is proposed for human face recognition. • The proposed method requires a lower number of features than KECA. • The highest level of discriminatory information is retained. • The algorithm is trained and tested on several benchmark face databases. • A deep learning CNN framework is also implemented for comparative analysis. [ABSTRACT FROM AUTHOR]

The article presents an introduction to the second issue on image-and video-based biometrics, published in February 1, 2004 issue of "IEEE Transactions in Circuits and Systems for Video Technology." In this issue, authors present a set of papers addressing relatively newer technologies including gait, human body dynamics, palmprint, paim-dorsa vein-patterns and multimodal algorithms. In the algorithm, the static and dynamic cues obtained from walking video are fused at the decision level using different combination rules and are shown to improve the performance of human identification and verification.

The article offers brief profiles of Tony Jebara, Greg Mori, Haesun Park, Ian Reid, Cristian Sminchisescu, and Massimo Tistarelli, new members of the journal's editorial board.