A method for eye detection based on SVD transforms

Which parts of an image evoke emotions in an observer? To answer this question, we introduce a novel problem in computer vision — predicting an Emotion Stimuli Map (ESM), which describes pixel-wise contribution to evoked emotions. Building a new image database, EmotionROI, as a benchmark for predicting the ESM, we find that the regions selected by saliency and objectness detection do not correctly predict the image regions which evoke emotion. Although objects represent important regions for evoking emotion, parts of the background are also important. Based on this fact, we propose using fully convolutional networks for predicting the ESM. Both qualitative and quantitative experimental results confirm that our method can predict the regions which evoke emotion better than both saliency and objectness detection.

Portable assistive technology systems are developed to enhance the capability of persons with disabilities. One of the most important senses for humans is vision. Vision is the most important sense which helps us in understanding the perception of the surrounding environment. Visually impaired persons face a lot of difficulty in understanding the perception around them, particularly in outdoor environment where objects are continuously changing and moving from one place to another. Object detection solutions would greatly assist visually impaired persons in avoiding from the barriers which they face in their daily routine life. The aim of the object detection system is to provide a simple, user-friendly, handy, economical and efficient solution for the visually impaired persons. Motive of this system is to develop a solution that detects the objects present using camera, as the input device in real time and communicate the same to the user using smartphone through headphones. The system would be using an audio device such as speakers or headphones in providing the information about objects to assist the visually impaired persons. The proposed system helps in identifying and avoiding the objects both in outdoor and indoor environments that affect day to day life activities and occupational performance of the visually impaired persons. The information about the objects in surrounding environment would be very much helpful to the visually impaired persons in their daily life.KeywordsObject detection systemVisually impairedBlindnessAssistive systemComputer visionImage recognition

The extraction of text information from traffic panels is one of the challenging problems in computer vision. Although the past decade has seen a promising shift and important progress in object detection, few works and a limited number of datasets focus specifically on extracting text from traffic signs. To address the lack of data for text detection in traffic panels, especially those with Arabic scripts, this paper introduces a new multilingual and multipurpose dataset named ASAYAR. It consists of three sub-datasets: Arabic-Latin scene text localization, traffic sign detection, and directional symbol detection. The dataset contains 1763 images collected on different Moroccan highways, and annotated manually, using 16 object categories. The fully annotated ASAYAR images contains more than 20000 bounding box objects. The paper also investigates the usability of the dataset, by evaluating the performance of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">state-of-the-art</i> algorithms for object and text detection. Experimental results show good detection scores, demonstrating the potential contribution of ASAYAR in the development of methods for text extraction from traffic panels.

Object detection is a crucial problem in computer vision that involves accurately locating objects in images or videos to identify instances to be detected. It has various applications, including facial detection, intelligent driving assistance, and satellite remote sensing detection. This review aims to aid researchers in quickly comprehending object detection. It covers the concept of object detection algorithms, analyzes the development history of object detection algorithms, and elaborates on the evolution process of object detection algorithms from independent development to combination with deep learning technology. The article divides object detection based on whether anchor boxes are generated during the detection process. It discusses the types of anchor boxes and non-anchor boxes and analyzes the current research status of single-stage and two-stage object detection algorithms. It also summarizes classic model structures in the development process of object detection algorithms and introduces difficulties in object detection. The article aims to solve the problem of object detection with a small number of samples and without detailed annotations during the training process. It summarizes and compares the advantages and disadvantages of various classic models, mainstream datasets, and evaluation indicators. Additionally, it looks forward to current challenges and future development directions in the field of object detection.

Object detection is a fundamental problem in computer vision, with applications spanning self-driving cars, surveillance systems, medical imaging, robotics, and smart cities. Among the myriad of algorithms developed for this task, the You Only Look Once (YOLO) family stands out for its ability to perform real-time and accurate object detection. This article provides a comprehensive analysis of the YOLO algorithm series, from YOLOv1 to YOLOv8, evaluating them across key performance metrics, including precision, recall, mean Average Precision (mAP), frames per second (FPS), and overall effectiveness. Unlike traditional two-stage detectors such as R-CNN, YOLO formulates object detection as a single regression problem: a single pass over the image simultaneously predicts bounding boxes and class probabilities. This end-to-end design enables YOLO to achieve high speed while maintaining a competitive accuracy-efficiency trade-off. We examine architectural innovations across YOLO versions, including batch normalization, anchor boxes, residual blocks, feature pyramid networks, and attention mechanisms, and discuss their impact on performance. Lightweight models (e.g., YOLOv5-Nano, YOLOv8-Small) are explored with a focus on their suitability for mobile and embedded systems, highlighting YOLO’s adaptability to resource-constrained environments. Challenges such as small object detection, occlusion, and domain-specific tuning are also addressed. This article serves as a practical guide for researchers, developers, and practitioners aiming to leverage YOLO for real-world object detection tasks.

Object detection, as one of the most fundamental and challenging problems in computer vision, has attracted much attention in recent years. Over the past two decades, we have witnessed the rapid technological evolution of object detection and its profound impact on the whole field of computer vision. In this paper, aortography key point detection approaches for transcatheter aortic valve implantation based on machine learning tools are discussed. The paper provides a description and analytical comparison of such popular methods as "object detection", "pose estimation". As a result of this study, a visual assessment system is proposed to facilitate the performance of the intervention procedure. The final accuracy of the proposed system reaches 79.3% with an analysis speed of 12 ms per image.

Object detection in hyperspectral images

Gender recognition from facial images has become one of challenging research problem in computer vision, security, verbal-nonverbal communication and human computer interaction applications nowadays. Because facial images include many information such as gender, facial expressions, age, ethnic origin in computer-aided applications, the success rate of the gender recognition depends on quality of facial images. In this paper, it is proposed a new gender recognition method combining Speed Up Robust Features (SURF) based Bags of Visual Words (BoW) and Support Vector Machine (SVM) algorithm unlike previous work. The method is tested on realistic frontal, left and right face images from modern gender recognition FERET dataset with 3560 samples to see efficiency of the proposed method. Experimental results show that the proposed method can obtain better gender recognition performance on FERET database and the accuracy level of on left and right face images is a bit lower than the average accuracy level of frontal ones.

IoT Enabled Automated Object Recognition for the Visually Impaired

Weakly supervised easy-to-hard learning for object detection in image sequences

Object detection is a fundamental problem in computer vision that involves detecting and localizing objects within an image. In recent years, convolutional neural networks (CNNs) have become the state-of-the-art approach for object detection tasks. YOLO (You Only Look Once) is a popular real-time object detection system that uses a single CNN to predict the bounding boxes and class probabilities for objects in an image. In this paper, we present a human detection and counting system using YOLO. Our system is trained on a large dataset of annotated images and achieves high accuracy and real-time performance. We evaluate our system on a public dataset and demonstrate its effectiveness in various scenarios. This paper focuses on developing a system for human detection and counting using YOLOv4 and Flask API. The system aims to address the increasing importance of human detection and counting in various fields such as social distancing, crowd management, and surveillance.

Generic object detection is one of the most fundamental problems in computer vision, yet it is difficult to provide all the bounding-box-level annotations aiming at large-scale object detection for thousands of categories. In this paper, we present a novel cross-supervised learning pipeline for large-scale object detection, denoted as CS-R-FCN. First, we propose to utilize the data flow of image-level annotated images in the fully-supervised two-stage object detection framework, leading to cross-supervised learning combining bounding-box-level annotated data and image-level annotated data. Second, we introduce a semantic aggregation strategy utilizing the relationships among the cross-supervised categories to reduce the unreasonable mutual inhibition effects during the feature learning. Experimental results show that the proposed CS-R-FCN improves the mAP by a large margin compared to previous related works.

A numerical simulation tool for predicting the impact of outdoor thermal environment on building energy performance

This chapter presents a method for image matching which is based on the detection and characterisation of curvilinear regions. In a curvilinear region, the ratio between its average width and its total length should be less than a predefined threshold. Besides, left and right borders should be locally parallel, it should exist a geometric similarity around the region axis and the colour along this axis should be homogeneous. That is, they constitute particular image structures and, therefore, the method is restricted to scenes where these particular items are presented. On the contrary, curvilinear regions automatically deform with changing viewpoint as to keep on covering identical physical parts of a scene. For this reason, they can be used as distinguished regions. The shape contour of these curvilinear regions is characterised using the adaptive curvature function. Experimental results show that this shape descriptor is invariant to rotation and translation, and partially invariant to noise and skewing. Scaling invariance is achieved by employing an extra normalisation stage. Thus, this descriptor, plus the region colour and position, can be used to match curvilinear regions detected on the input image with those previously stored in a database. This is the basis of the correspondence algorithm described in this paper: the similarity index between two images is determined by the presence of the same set of curvilinear regions localised in similar positions. There are many directions for further research. One of this is the integration of several types of distinguished regions. As we commented above, the obligatory presence of these regions in the images is the main disadvantage of the proposed system. Besides, further work must be accomplished in the correspondence algorithm to employ the most similar region correspondences as ground control points. These points could be used to generate a fundamental matrix describing the geometric constraints between the two images. Thus, matches that did not agree with the majority solution could be removed.

Object detection is one of the major problems in computer vision, and has been extensively studied. Most of the existing detection works rely on labor-intensive supervision, such as ground truth bounding boxes of objects or at least image-level annotations. On the contrary, we propose an object detection method that does not require any form of human annotation on target tasks, by exploiting freely available web images. In order to facilitate effective knowledge transfer from web images, we introduce a multi-instance multi-label domain adaption learning framework with two key innovations. First of all, we propose an instance-level adversarial domain adaptation network with attention on foreground objects to transfer the object appearances from web domain to target domain. Second, to preserve the class-specific semantic structure of transferred object features, we propose a simultaneous transfer mechanism to transfer the supervision across domains through pseudo strong label generation. With our end-to-end framework that simultaneously learns a weakly supervised detector and transfers knowledge across domains, we achieved significant improvements over baseline methods on the benchmark datasets.