Regional Feature Vectors Research Articles

Visual Memory Neural Network for Artistic Graphic Design

Artistic graphic design is the aesthetic result of the designer’s fusion of various elements, with a high degree of independence. Considering the lack of significant visual design scope and aesthetic indicators of graphic design, our research aims to build an upgraded network model that can categorize different types of artistic graphics with labels and realize the free combination of graphic solutions. We realize the scheme reorganization of artistic graphic design from the perspective of computer vision and propose the artistic graphic design method based on memory neural network. We built a computer vision environment and reconstructed the computer vision network to set up an independent deep camera vision range calculation law. Considering the artistic graphic region segmentation problem, we propose the self-attentive mechanism, which can quantitatively segment different artistic graphic regions according to temporal features, before arranging them in a sequence to obtain the graphic region feature vector. We also add the LSTM structure based on the attention mechanism to match with the self-attention features of the graphical region segmentation module and pass the matched attention feature vector to the LSTM network to extract the labeled text feature information of the graphs. To test the effectiveness of our method, we build a database of artistic graphics and set up an adaptive training process. We also compared deep learning methods of the same type, and the experimental results proved that our method outperformed other deep methods in artistic graphic design by keeping the scheme reorganization accuracy and quantitative evaluation of artistic models above 90%.

Fast Recognition and Location Method of Parts for Assembly Robot Based on Deep Learning Network

Aiming at the problems of low components recognition rate and poor robustness under complex conditions such as adhesion, stacking and light source interference, a fast recognition and position method of parts based on deep convolution neural network was proposed. A multilayer convolution neural network for real-time detection of parts was constructed. Firstly, multi-scale extraction of candidate regions for target images was carried out. Then,the feature vectors of candidate candidate regions were automatically extracted by convolution neural network. The softmax classifier was used to recognize and locate. The experimental results show that the method realized the recognition and location for multiple parts in complex environment. The accuracy rate is over 95.2%.The detection took only 78ms.The accuracy rate and real-time performance of the method proved its feasibility.

Detection of cow mounting behavior using region geometry and optical flow characteristics

Computer vision technology may improve the accuracy and robustness of estrus detection in dairy cows. In this study, we proposed a method for detecting mounting behavior in dairy cattle using the geometric and optical flow characteristics of identified image regions in videos taken on dairy farms. Videos captured on a farm often have a complex background, which can interfere with target detection. In this study, we used masking technology to remove the unrelated background, converted the RGB color space to HSV color space, and adjusted the summation coefficients of the HSV channels to improve the contrast between the cows and background images. Subsequently, the proposed Background Subtraction with Color and Texture Features (BSCTF) algorithm was used to detect cow regions. Then, to perform inter-frame differential processing on detection regions, the geometric and optical flow characteristics of the regions were extracted, and seven optimized features were used to construct regional feature vectors. Finally, a support vector machine (SVM) classifier was trained to classify the detected regions into mounting regions and non-mounting regions, which allowed the identification of mounting behavior. We obtained accuracy and omission rates of this detection method of 98.3% and 6.4%, respectively. The average recognition accuracy and false positive rates of the SVM classifier were 90.9% and 4.2%, respectively. These results demonstrate that the proposed method is effective for detecting the mounting behavior of dairy cows and is convenient for further judging whether cows are in estrus.

A Prototype Selection Method for Synthetic Object Recognition Based on Structural Graph Model

At present, many popular methods for object recognition are based on regional visual feature vectors which ignore the global structure of the object, leading to the “semantic gap” between image content and its tag. Graph-based structural representation of an object can record regional visual features and global relationships between regions that compose the object. However, the computation complexity of graph comparing limits the application of graph model in object recognition. One method for improving the recognizing speed is to reduce the prototypes in concept space of specific object category. We presented a prototype selection method for sythetic object recognition based on structural graph model using greedy clustering and representative sample selection. Experiments are conducted on a 2-D CAD synthetic object database. And the results show that the prototype selection method reduces the time cost of object recognition greatly with an acceptable loss in accuracy.

Content based image retrieval based on relative locations of multiple regions of interest using selective regions matching

In this study, a novel technique for image retrieval based on selective regions matching using region codes is presented. All images in the database are uniformly divided into multiple regions and each region is assigned a 4-bit region code based upon its location relative to the central region. Dominant color and Local Binary Pattern (LBP) based texture features are extracted from these regions. Feature vectors together with their region codes are stored and indexed in the database. During retrieval, feature vectors of regions having region codes similar to the query image region are used for comparison. To reflect the user’s intent in query formulation in a better way, an effective technique for Region of Interest (ROI) overlapping block selection is also proposed. Region codes are further used to find relative locations of multiple ROIs in query and target images. The performance of the proposed approach is tested on the MPEG-7 CCD database and Corel image database. Experimental results show that the proposed approach increases the accuracy and reduces image retrieval time.

Automatic detection of road traffic signs from natural scene images based on pixel vector and central projected shape feature

Considering the problem of automatic information acquisition in the field of intelligent transportation system (ITS), a new approach for detection of road traffic sign from natural scene images is proposed in this study. The adaptive colour segmentation based on pixel vector is firstly used to segment colour image into binary image and stand out traffic sign regions, which can reduce the influence of lighting conditions on image segmentation. Secondly, to improve the ability of shape identification during traffic sign detection, central projection transformation (CPT) is used to compute shape feature vectors of different candidate regions, and this shape feature is input to the probabilistic neural networks (PNN) to discriminate true traffic signs from candidates. The proposed approach is applied to many natural images. Experimental results show that the proposed method can effectively detect road traffic signs from natural scene images.

영역 특징벡터를 이용한 내용기반 영상검색

본 논문은 기존의 컬러 히스토그램 방법들의 단점을 극복하고자 영역 특징백터를 이용한 영상 검색 방법을 제안한다. 컬러 히스토그램 검색방법들은 양자화 오류 등의 이유로 정확성이 떨어지는 단점이 있다 이를 해결하기 위해 제안 방법은 색상 정보를 HSY 공간으로 변환하여 순수 색상 정보인 hue 성분만을 양자화하여 히스토그램을 구하고, 이를 명암, 이동, 회전등에 강인한 검색 특징으로 사용한다. 또한 컬러 히스토그램 방법들의 가장 큰 문제점인 공간 정보가 부족한 것은 영상을 16개 영역으로 나눠서 각 영역간의 비교를 통해 해결한다. 그리고 색상 검색에 추가적으로 모양 특징인 에지와 질감 특징인 DCT 변환의 DC를 이용하여 검색의 정확도를 높인다 1,000개의 컬러 영상을 사용해 실험한 결과 기존의 방법들 보다 좋은 정확성을 보인다. This paper proposes a method of content-based image retrieval using region feature vector in order to overcome disadvantages of existing color histogram methods. The color histogram methods have a weak point that reduces accuracy because of quantization error, and more. In order to solve this, we convert color information to HSV space and quantize hue factor being purecolor information and calculate histogram and then use thus for retrieval feature that is robust in brightness, movement, and rotation. Also we solve an insufficient part that is the most serious problem in color histogram methods by dividing an image into sixteen regions and then comparing each region. We improve accuracy by edge and DC of DCT transformation. As a result of experimenting with 1,000 color images, the proposed method has showed better precision than the existing methods.

HIRBIR: A hierarchical approach to region-based image retrieval

This paper proposes a hierarchical approach to region-based image retrieval (HIRBIR) based on wavelet transform whose decomposition property is similar to human visual processing. First, automated image segmentation is performed fast in the low-low (LL) frequency subband of the wavelet domain that shows the desirable low image resolution. In the proposed system, boundaries between segmented regions are deleted to improve the robustness of region-based image retrieval against segmentation-related uncertainty. Second, a region feature vector is hierarchically represented by information in all wavelet subbands, and each feature component of a feature vector is a unified color---texture feature. Such a feature vector captures well the distinctive features (e.g., semantic texture) inside one region. Finally, employing a hierarchical feature vector, the weighted distance function for region matching is tuned meaningfully and easily, and a progressive stepwise indexing mechanism with relevance feedback is performed naturally and effectively in our system. Through experimental results and comparison with other methods, the proposed HIRBIR shows a good tradeoff between retrieval effectiveness and efficiency as well as easy implementation for region-based image retrieval.

A Region Dissimilarity Relation That Combines Feature-Space and Spatial Information for Color Image Segmentation

This paper proposes a methodology that incorporates principles from cluster analysis and graph representation to achieve efficient image segmentation results. More specifically, a feature-based, inter-region dissimilarity relation is considered here in order to determine the dissimilarity matrix in a graph-based segmentation scheme. The calculation of the dissimilarity function between adjacent elementary image regions is based on the proximity of each region's feature vector to the main clusters that are formed by the image samples in the feature space. In contrast to typical segmentation approaches of the literature, the global feature space information is included in the spatial graph representation that was derived from the initial Watershed partitioning. A region grouping process is applied next to form the final segmentation results. The proposed approach was also compared to approaches that use feature-based, or spatial information exclusively, to indicate its effectiveness.

A modular clutter rejection technique for FLIR imagery using region-based principal component analysis

A modular clutter-rejection technique that uses region-based principal component analysis (PCA) is proposed. A major problem in FLIR ATR is the poorly centered targets generated by the preprocessing stage. Our modular clutter-rejection system uses static as well as dynamic region of interest (ROI) extraction to overcome the problem of poorly centered targets. In static ROI extraction, the center of the representative ROI coincides with the center of the potential target image. In dynamic ROI extraction, a representative ROI is moved in several directions with respect to the center of the potential target image to extract a number of ROIs. Each module in the proposed system applies region-based PCA to generate the feature vectors, which are subsequently used to make a decision about the identity of the potential target. Region-based PCA uses topological features of the targets to reject false alarms. In this technique, a potential target is divided into several regions and a PCA is performed on each region to extract regional feature vectors. We propose using regional feature vectors of arbitrary shapes and dimensions that are optimized for the topology of a target in a particular region. These regional feature vectors are then used by a two-class classifier based on the learning vector quantization to decide whether a potential target is a false alarm or a real target. We also present experimental results using real-life data to evaluate and compare the performance of the clutter-rejection systems with static and dynamic ROI extraction.

A clutter rejection technique for FLIR imagery using region based principal component analysis

Abstract A modular clutter-rejection technique that uses region-based principal component analysis (PCA) is proposed. A major problem in FLIR ATR is the poorly centered targets generated by the preprocessing stage. Our modular clutter-rejection system uses static as well as dynamic region of interest (ROI) extraction to overcome the problem of poorly centered targets. In static ROI extraction, the center of the representative ROI coincides with the center of the potential target image. In dynamic ROI extraction, a representative ROI is moved in several directions with respect to the center of the potential target image to extract a number of ROIs. Each module in the proposed system applies region-based PCA to generate the feature vectors, which are subsequently used to make a decision about the identity of the potential target. Region-based PCA uses topological features of the targets to reject false alarms. In this technique, a potential target is divided into several regions and a PCA is performed on each region to extract regional feature vectors. We propose using regional feature vectors of arbitrary shapes and dimensions that are optimized for the topology of a target in a particular region. These regional feature vectors are then used by a two-class classifier based on the learning vector quantization to decide whether a potential target is a false alarm or a real target. We also present experimental results using real-life data to evaluate and compare the performance of the clutter-rejection systems with static and dynamic ROI extraction.