Deep Convolutional Sequence Approach Towards Real-Time Intelligent Optical Scanning

Visual text recognition is the most dynamic computer vision application due to its rising demand in several applications like crime scene detection, assisting blind people, digitizing, book scanning, etc. However, numerous research works were executed on static visuals having organized text and on captured video frames in the past. The key objective of this study is to develop the real-time intelligent optical scanner that will extract every sequence of text from high-speed video, noisy visual input, and offline handwritten script. The scientific work has been carried out with the combination of multiple deep learning approaches, namely EAST, CNN, and Bi-LSTM with CTC. The system is trained and tested on four public datasets (i.e., ICDAR 2015, SVT, Synth-Text, IAM-3.0) and measured on the basis of recall, precision, and f-measure. Based on the challenges, performance has been examined under three different categories, and the outcomes are optimistic and encouraging for future advancement.

A Low Redundancy Wavelet Entropy Edge Detection Algorithm

Fast edge detection of images can be useful for many real-world applications. Edge detection is not an end application but often the first step of a computer vision application. Therefore, fast and simple edge detection techniques are important for efficient image processing. In this work, we propose a new edge detection algorithm using a combination of the wavelet transform, Shannon Entropy and thresholding. The new algorithm is based on the concept that each Wavelet decomposition level has an assumed level of structure that enables the use of Shannon entropy as a measure of global image structure. The proposed algorithm is developed mathematically and compared to five popular edge detection algorithms. The results show that our solution is low redundancy, noise resilient, and well suited to real-time image processing applications.

Ensemble Learning based Age Invariant Fea-ture Recognition Using Soft Computing

Face recognition system is a state-of-the-art computer vision application within the artificial intelligence arena. Face recognition is the automated recognition of humans for their names/unique ID. The age invariant face recognition is a challenge task in the field of face recog-nition. In this work, we have introduced a stacked support vector machine where kernel activation of prototype examples is combined in nonlinear ways. The proposed work integrates soft compu-ting-based support vector machine (SVM) with deep SVM. The proposed model uses the implied relation between the variables described above in order to optimize their overall performance. Specifically, our method uses three different stages of complex convolution neural networks that detect and analyze the location of faces position and landmarks. This work has introduced cross-age celebrity dataset (CACD) for both single as well as cross-database enabling the transition of age. The proposed work has been implemented in the MATLAB simulation tool considering CACD dataset. Experimental results indicate that our techniques significantly outperform other strategies across a range of challenging metrics.

Designing a Computer-Vision Application: A Case Study for Hand-Hygiene Assessment in an Open-Room Environment

Hand-hygiene is a critical component for safe food handling. In this paper, we apply an iterative engineering process to design a hand-hygiene action detection system to improve food-handling safety. We demonstrate the feasibility of a baseline RGB-only convolutional neural network (CNN) in the restricted case of a single scenario; however, since this baseline system performs poorly across scenarios, we also demonstrate the application of two methods to explore potential reasons for its poor performance. This leads to the development of our hierarchical system that incorporates a variety of modalities (RGB, optical flow, hand masks, and human skeleton joints) for recognizing subsets of hand-hygiene actions. Using hand-washing video recorded from several locations in a commercial kitchen, we demonstrate the effectiveness of our system for detecting hand hygiene actions in untrimmed videos. In addition, we discuss recommendations for designing a computer vision system for a real application.

IMPROVING RANSAC FEATURE MATCHING BASED ON GEOMETRIC RELATION

Feature Matching between images is an essential task for many computer vision and photogrammetry applications, such as Structure from Motion (SFM), Surface Extraction, Visual Simultaneous Localization and Mapping (VSLAM), and vision-based localization and navigation. Among the matched point pairs, there are typically false positive matches. Therefore, outlier detection and rejection are important steps in any vision application. RANSAC has been a well-established approach for outlier detection. The outlier ratio and the number of required correspondences used in RANSAC determine the number of iterations needed, which ultimately, determines the computation time. We propose a simple algorithm (GR_RANSAC) based on the two-dimensional spatial relationships between points in the image domain. The assumption is that the distances and bearing angles between the 2D feature points should be similar in images with small disparity, such as the case for video image sequences. In the proposed approach, the distances and angles are measured from a reference point in the first image and its correspondence in the other image, and the points with any significant differences are considered as outliers. This process can pre-filter the matched points, and thus increase the inliers’ ratio. As a result, GR_RANSAC can converge to the correct hypothesis in fewer trial runs than ordinary RANSAC.