МОДЕЛЬ И МЕТОД ДЛЯ ОБНАРУЖЕНИЯ ЦВЕТНЫХ ОБЪЕКТОВ С ИСПОЛЬЗОВАНИЕМ МОДИФИЦИРОВАННОГО ПРЕОБРАЗОВАНИЯ РАДИАЛЬНОЙ СИММЕТРИИ

We present an approach for detecting and estimating the 3D poses of objects\nin images that requires only an untextured CAD model and no training phase for\nnew objects. Our approach combines Deep Learning and 3D geometry: It relies on\nan embedding of local 3D geometry to match the CAD models to the input images.\nFor points at the surface of objects, this embedding can be computed directly\nfrom the CAD model; for image locations, we learn to predict it from the image\nitself. This establishes correspondences between 3D points on the CAD model and\n2D locations of the input images. However, many of these correspondences are\nambiguous as many points may have similar local geometries. We show that we can\nuse Mask-RCNN in a class-agnostic way to detect the new objects without\nretraining and thus drastically limit the number of possible correspondences.\nWe can then robustly estimate a 3D pose from these discriminative\ncorrespondences using a RANSAC- like algorithm. We demonstrate the performance\nof this approach on the T-LESS dataset, by using a small number of objects to\nlearn the embedding and testing it on the other objects. Our experiments show\nthat our method is on par or better than previous methods.\n

An MRF model-based approach to the detection of rectangular shape objects in color images

Extraction of man-made objects in images is an important problem in computer vision. In this paper a general procedure to extract man-made objects in colour images of terrains using ‘Background Discriminant Transformation (BDT)’ is presented. BDT is simple to use, robust and application oriented. The procedure does not require extensive knowledge of the terrain. To illustrate the procedure an example is shown from an airborne colour image. However, the process has been also tested, and reported elsewhere, on spaceborne multispectral images such SPOT images. At this stage the process is semi-automatic.

The authors describe how to design color illumination to improve the discriminability of objects in color images. This procedure is useful in applications where the illumination can be controlled, such as inspection tasks. From the physics of color image formation, the optimal color illumination for discriminating materials is derived using a parametrically defined set of illuminants. The authors suggest how such an approach might be extended to sets of materials and more general classes of light sources. Experiments with painted color patches and live potato plantlets are used to illustrate the usefulness of actively controlling illumination color in machine vision. >

Object detection and localization in a video stream is an important requirement for almost all vision systems. In the article a design embedded into a reconfigurable device which is using the Histogram of Oriented Gradients for feature extraction and SVM classification for detecting multiple objects is presented. Superior accuracy is achieved by making all computations using single precision 32-bit floating point values in all stages of image processing. The resulting implementation is fully pipelined and there is no need for external memory. Finally a working system able to detect and localize three different classes of objects in color images with resolution 640×480 @ 60fps is presented with a computational performance above 9 GFLOPS.

Object recognition technologies using PCA(principal component analysis) recognize objects by deciding representative features of objects in the model image, extracting feature vectors from objects in an image and measuring the distance between them and object representation. Given frequent recognition problems associated with the use of point-to-point distance approach, this study adopted the K-Nearest Neighbor technique(class-to-class) in which a group of object models of the same class is used as recognition unit for the images inputted on a continual input image. However, we propose the object recognition technique new PCA analysis method that discriminates an object in database even in the case that the variation of illumination in training images exists. Object recognition algorithm proposed here represents more enhanced recognition rate to change of illumination than existing methods.KeywordsObject RecognitionRecognition RateHistogram EqualizationVariable IlluminationRecognition UnitThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Object recognition technologies using PCA(principal component analysis) recognize objects by deciding representative features of objects in the model image, extracting feature vectors from objects in an image and measuring the distance between them and object representation. Given frequent recognition problems associated with the use of point-to-point distance approach, this study adopted the K-nearest neighbor technique(class-to-class) in which a group of object models of the same class is used as recognition unit for the images inputted on a continual input image. However, we propose the object recognition technique new PCA analysis method that discriminates an object in database even in the case that the variation of illumination in training images exists. Object recognition algorithm proposed here represents more enhanced recognition rate to change of illumination than existing methods.

The topic of colour image segmentation has been and still is a hot issue in areas such as computer vision and image processing because of its wide range of practical applications. The urge has led to the development of numerous colour image segmentation algorithms to extract salient objects from colour images. However, because of the diverse imaging conditions in varying application domains, accuracy and robustness of several state-of-the-art colour image segmentation algorithms still leave room for further improvement. This dissertation reports on the development of a new image segmentation algorithm based on perceptual colour difference saliency along with binary morphological operations. The algorithm consists of four essential processing stages which are colour image transformation, luminance image enhancement, salient pixel computation and image artefact filtering. The input RGB colour image is first transformed into the CIE L*a*b colour image to achieve perceptual saliency and obtain the best possible calibration of the transformation model. The luminance channel of the transformed colour image is then enhanced using an adaptive gamma correction function to alleviate the adverse effects of illumination variation, low contrast and improve the image quality significantly. The salient objects in the input colour image are then determined by calculating saliency at each pixel in order to preserve spatial information. The computed saliency map is then filtered using the morphological operations to eliminate undesired factors that are likely present in the colour image. A series of experiments was performed to evaluate the effectiveness of the new perceptual colour difference saliency algorithm for colour image segmentation. This was accomplished by testing the algorithm on a large set of a hundred and ninety images acquired from four distinct publicly available benchmarks corporal. The accuracy of the developed colour image segmentation algorithm was quantified using four widely used statistical evaluation metrics in terms of precision, F-measure, error and Dice. Promising results were obtained despite the fact that the experimental images were selected from four different corporal and in varying imaging conditions. The results have indeed demonstrated that the performance of the newly developed colour image segmentation algorithm is consistent with an improved performance compared to a number of other saliency and non- saliency state-of-the-art image segmentation algorithms.

In this paper a new application of information retrieval techniques for detecting moving objects in multiple & color images is presented. We have used circular shift moments extraction from gray scale images. For color images color edges were used as features. This technique makes use of a color edge detection scheme, applied to the difference between current and reference frame. The edges are filled using morphological filtering of dilations and erosions. The images used were taken from a stationary camera. In this work, a statistical decision rule is proposed to cope with the effects of noise. Moreover the computation time is compared with shading model method. All the techniques are tested under constant illumination, varying illumination, Gaussian noise, and speckle noise. The performance is compared on the basis of parameters such as detection rate, false rate, misses rate, and percentage correct classification. Simulation results show that, circular shift moments and color edges retrieve changes in moving objects accurately under time varying illumination case.

The article deals with the problem of recognition of road infrastructure objects. The expediency of solving this problem using an intelligent system based on a neural network model is updated. In order to develop it to solve the problem, the collection of initial data, the choice of libraries, the construction of the architecture and the training of the neural network were carried out. The initial data for constructing the model was obtained when generating queries in a search engine and presented in the form of images of traffic signs, road markings and traffic lights. To build a neural network, training and test samples were formed for 3 classes of images, taking into account various road conditions. When building a neural network model, the activation functions Relu and Softmax were used. The neural network model includes 4 convolution layers, 2 subsampling layers and 2 fully connected layers. The IDLE development environment was used to train the model. The program code is written in the Python 3.9.7 programming language. The neural network model was trained for 10 epochs. An example of the operation of the system and the results of testing the developed built model are presented. Studies have shown that the developed intelligent system and neural network model for recognizing road infrastructure objects are adequate, and its accuracy is 92 %. The achieved accuracy of the neural network model is not inferior to the accuracy of other methods for classifying objects in images.

Perceptual localization and focus refinement network for RGB-D salient object detection

Object detection is a technology that automatically identifies and locates specific objects in images or videos and plays a core role in various fields, such as autonomous driving, security surveillance, and medical imaging. You Only Look Once (YOLO) has gained attention for achieving both accuracy and detection speed in real-time applications; however, during the resolution reduction process, detailed information is lost, and unnecessary signals are mixed in the multi-scale feature fusion stage, resulting in limited detection performance for small objects and complex background scenes. To alleviate these limitations, we propose YOLO-RECAP, which integrates a Content-Aware ReAssembly of FEatures (CARAFE) and Efficient Channel Attention (ECA) modules based on YOLOv11. CARAFE precisely restores boundaries and shapes during the upsampling stage by utilizing position-specific content information, whereas ECA effectively models interchannel interactions to emphasize important signals. For performance verification, VisDrone2019, Store Keeping Unit-110 K (SKU-110 K), Pascal Visual Object Classes (VOC), and Dataset for Object Detection in Aerial Images (DOTA)v1 were used. In addition, Latency is reported under an end-to-end setting that includes pre-processing, inference, and post-processing, to reflect practical deployment conditions. As a result, YOLO-RECAP achieved mAP50 of 0.316, mAP50@95 of 0.184, Latency of 16.5 ms, and 60.6 FPS on VisDrone2019; mAP50 of 0.895, mAP50@95 of 0.572, Latency of 18.2 ms, and 55.0 FPS on SKU-110 K; mAP50 of 0.770, mAP50@95 of 0.561, Latency of 15.6 ms, and 63.9 FPS on Pascal VOC; and mAP50 of 0.281, mAP50@95 of 0.157, Latency of 18.6 ms, and 53.9 FPS on DOTAv1. Qualitative bounding-box visualizations further indicate reduced missed detections and more stable predictions in cluttered and densely populated scenes. As a result, YOLO-RECAP provided a more stable and balanced detection performance than the existing YOLOv11 and recent detection models, especially for small objects and complex backgrounds. This code is available at https://github.com/Heon-ju/YOLO-RECAP.git .

Sea turtles exhibit high migratory rates and occupy a broad range of habitats, which in turn makes monitoring these taxa challenging. Applying deep learning (DL) models to vast image datasets collected from citizen science programs can offer promising solutions to overcome the challenge of monitoring the wide habitats of wildlife, particularly sea turtles. Among DL models, object detection models, such as the You Only Look Once (YOLO) series, have been extensively employed for wildlife classification. Despite their successful application in this domain, detecting objects in images with complex backgrounds, including underwater environments, remains a significant challenge. Recently, instance segmentation models have been developed to address this issue by providing more accurate classification of complex images compared to traditional object detection models. This study compared the performance of two state-of-the-art DL methods namely; the object detection model (YOLOv5) and instance segmentation model (YOLOv5-seg), to detect and classify sea turtles. The images were collected from iNaturalist and Google and then divided into 64% for training, 16% for validation, and 20% for test sets. Model performance during and after finishing training was evaluated by loss functions and various indexes, respectively. Based on loss functions, YOLOv5-seg demonstrated a lower error rate in detecting rather than classifying sea turtles than the YOLOv5. According to mean Average Precision (mAP) values, which reflect precision and recall, the YOLOv5-seg model showed superior performance than YOLOv5. The mAP0.5 and mAP0.5:0.95 for the YOLOv5 model were 0.885 and 0.795, respectively, whereas for the YOLOv5-seg, these values were 0.918 and 0.831, respectively. In particular, based on the loss functions and classification results, the YOLOv5-seg showed improved performance for detecting rather than classifying sea turtles compared to the YOLOv5. The results of this study may help improve sea turtle monitoring in the future.

Automatic Identification of vehicles is a very challenging area, which is in contrast to the traditional practice of monitoring the vehicles manually. Automatic license plate (LP) recognition is one of the most promising aspects of applying computer vision techniques towards intelligent transportation system. In Location of the vehicle plate, a method of vehicle license plate character segmentation and extraction based on improved edge detection and Mathematical morphology was presented. In the first place, color images were changed into grey images, secondly through calculates the difference of each pixel and neighbourhood pixels to build up images edge and it can make the license plate stand out; Sobel operator is used to extract the edge of objects in image; then the algorithm applies the dilation and erosion mathematical morphology of binary images to get the image smooth contour. The segmentation result which is sent forward to LP recognition stage will improve further processing’s efficiency . Neural Network is used to recognize the license plate character. Because of the accuracy of the plate region extraction, the character can be extracted exactly from the plate region .

The growing capacity of computers, the abundance of digital cameras and the increased connectivity of the world all point to large digital multimedia archives. They include images and videos from the World Wide Web, museum objects, flowers, trademarks, and views from everyday life. The faster they grow, the more prominently needed is the efficient access to the content of the images and videos. In this paper we have given important step of feature extraction, will be discussed in detail such as color, shape and texture information, particularly paying attention to discriminatory power and invariance. Then, we focus on the concepts of indexing and genre classification as intermediate step to sort the data. We pay attention to (interactive) ways to perform browsing and retrieval by means of information visualization and relevance feedback. Methods are being discussed to localize the retrieved objects in images. We adopt a hybrid approach for such text extraction by exploiting a number of characteristics of text blocks in color images and video frames. Our system detects both caption text as well as scene text of different font, size, color and intensity. Such texts are used for retrieval of video clips based on any given keyword. Content-Based Image And Video Retrieval addresses the basic concepts and techniques for designing content-based image and video retrieval systems.