Survey of Object Detection Methods in Camouflaged Image

Camouflage is an attempt to conceal the signature of a target object into the background image. Camouflage detection methods or Decamouflaging method is basically used to detect foreground object hidden in the background image. In this research paper authors presented survey of camouflage detection methods for different applications and areas

Hand Gesture Recognition as Password to Open the Door with Camera and Convexity Defect Method

Computer Vision is one of reasearch that gets a lot of attention with many applications. One of the application is the hand gesture recognition system. By using EmguCV, will be obtained camera images from webcam camera. The Pictures will be disegmented by using skin detection method for decrease noises in order to obtain the information needed. The final project of this system is to implement the convexity defect method for extracting images and recognize patterns of hand gesture that represent the characters A, B, C, D, and E. The parameters used in pattern recognition of hand gesture is the number and length of the line connecting the hull and defects derived from the pattern of hand gesture

Hand Gesture Recognition as Password to Open The Door With Camera and Convexity Defect Method

Computer Vision is one of reasearch that gets a lot of attention with many applications. One of the application is the hand gesture recognition system. By using EmguCV, will be obtained camera images from webcam camera. The Pictures will be disegmented by using  skin detection method for decrease noises in order to obtain the information needed. The final project of this system is to implement the convexity defect method for extracting images and recognize patterns of hand gesture that represent the characters A, B, C, D, and E. The parameters used in pattern recognition of hand gesture is the number and length of the line connecting the hull and defects derived from the pattern of hand gesture

Transformer Transforms Salient Object Detection and Camouflaged Object Detection

The transformer networks are particularly good at modeling long-range dependencies within a long sequence. In this paper, we conduct research on applying the transformer networks for salient object detection (SOD). We adopt the dense transformer backbone for fully supervised RGB image based SOD, RGB-D image pair based SOD, and weakly supervised SOD within a unified framework based on the observation that the transformer backbone can provide accurate structure modeling, which makes it powerful in learning from weak labels with less structure information. Further, we find that the vision transformer architectures do not offer direct spatial supervision, instead encoding position as a feature. Therefore, we investigate the contributions of two strategies to provide stronger spatial supervision through the transformer layers within our unified framework, namely deep supervision and difficulty-aware learning. We find that deep supervision can get gradients back into the higher level features, thus leads to uniform activation within the same semantic object. Difficulty-aware learning on the other hand is capable of identifying the hard pixels for effective hard negative mining. We also visualize features of conventional backbone and transformer backbone before and after fine-tuning them for SOD, and find that transformer backbone encodes more accurate object structure information and more distinct semantic information within the lower and higher level features respectively. We also apply our model to camouflaged object detection (COD) and achieve similar observations as the above three SOD tasks. Extensive experimental results on various SOD and COD tasks illustrate that transformer networks can transform SOD and COD, leading to new benchmarks for each related task. The source code and experimental results are available via our project page: https://github.com/fupiao1998/TrasformerSOD.Comment: Technical report, 18 pages, 22 figure

Advances in Deep Concealed Scene Understanding

Concealed scene understanding (CSU) is a hot computer vision topic aiming to perceive objects exhibiting camouflage. The current boom in terms of techniques and applications warrants an up-to-date survey. This can help researchers to better understand the global CSU field, including both current achievements and remaining challenges. This paper makes four contributions: (1) For the first time, we present a comprehensive survey of deep learning techniques aimed at CSU, including a taxonomy, task-specific challenges, and ongoing developments. (2) To allow for an authoritative quantification of the state-of-the-art, we offer the largest and latest benchmark for concealed object segmentation (COS). (3) To evaluate the generalizability of deep CSU in practical scenarios, we collect the largest concealed defect segmentation dataset termed CDS2K with the hard cases from diversified industrial scenarios, on which we construct a comprehensive benchmark. (4) We discuss open problems and potential research directions for CSU. Our code and datasets are available at https://github.com/DengPingFan/CSU, which will be updated continuously to watch and summarize the advancements in this rapidly evolving field.Comment: 18 pages, 6 figures, 8 table

Розпізнавання стратегічних технічних об'єктів за допомогою згорткових нейронних мереж

Дипломна робота: 119 с., 42 рис., 1 додаток, 43 джерел Мета роботи – використати апарат згорткових штучних нейронних мереж для розпізнавання стратегічних технічних об'єктів. Об’єктом дослідження є методи та моделі розпізнавання об’єктів. Пpедметом досліджень є системи розпізнавання стратегічних технічних об’єктів на основі глибоких згорткових нейронних мереж. Актуальність даного методу випливає із стратегічної важливості швидкого та точного розпізнавання, обробки та аналізу візуальної інформації із камер дронів, наземної техніки та об’єктів та інших джерел. Така інформація може бути життєво важливою як у військовій, так і у цивільній сферах, наприклад для журналістики, своєчасного попередження мирних громадян про небезпеку та їх захисту. У даній роботі запропонована модель для розпізнавання стратегічних технічних об'єктів на основі глибоких згорткових нейронних мереж, орієнтована на використання на БПЛА різних класів та за умов значно обмежених обчислювальних ресурсів.Thesis: 119 p., 42 fig., 1 appendice, 43 sources The purpose of this work is to use the apparatus of convolutional artificial neural networks for recognition of strategic technical objects. The object of research is methods and models of object recognition. The subject of research is recognition systems of strategic technical objects based on deep convolutional neural networks. The relevance of this method stems from the strategic importance of rapid and accurate recognition, processing and analysis of visual information from drone cameras, ground vehicles and objects and other sources. Such information can be vital in both the military and civilian areas, such as journalism, the timely warning of civilians of danger and their protection. In this thesis, a model for recognizing strategic technical objects based on deep convolutional neural networks is proposed, aimed at use on UAVs of various classes and under conditions of significantly limited computing resources

for Geometric Computing, and the Moscona fund.

Camouflage is frequently used by animals and humans (usually for military purposes) in order to conceal objects from visual surveillance or inspection. Most camouflage methods are based on superpositioning multiple edges on the object that is supposed to be hidden, such that its familiar contours and texture are masked. In this work, we present an operator, (Darg), that is applied directly to the intensity image in order to detect 3D smooth convex (or equivalently: concave) objects. The operator maximally responds to a local intensity configuration that corresponds to curved 3D objects, and thus, is used to detect curved objects on a relatively flat background, regardless of image edges, contours and texture. In that regard, we show that a typical camouflage found in some animal species, seems to be a ”counter measure ” taken against detection that might be based on our method. Detection by Darg is shown to be very robust, from both theoretic considerations and practical examples of real-life images. As a part of the camouflage breaking demonstration, Darg, which is non-edge-based, is compared with a representative edge-based operator. Better performance is maintained by Darg for both animal and military camouflage breaking. Key Words: convexity detection, regions of interest, camouflage breaking, counter shading. CONVEXITY-BASED VISUAL CAMOUFLAGE BREAKING