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Human action recognition is the process of labeling image sequences with action labels. Robust solutions to this problem have applications in domains such as medical care, human-computer interaction and virtual training. The task is challenging for feature extraction due to variations in motion performance, recording settings and inter-personal differences. To meet these challenges, we propose two...
We present an application of a Multiple Instance Learning (MIL) approach to image classification. In particular we focus on a recent MIL method for binary classification where the objective is to discriminate between positive and negative sets of points. Such sets are called bags and the points inside the bags are called instances. In the case of two classes of instances (positive and negative), a...
Wireless capsule endoscopy (WCE) is a contemporary method that can view the entire intestine. Because of its advantages, it has been widely used. However, physicians demand an automatic way to shorten the time required to analyze the produced images (over 55,000 images in one examination for one patient). In this paper a recognition system for CE imaging has been developed to automatically detect...
There are many species of tomato diseases and pests, and the pathology of which is complex. It is difficult and error-prone to simply rely on manual identification. For the ten most common tomato diseases and pests in China, This paper explores the detection algorithms on leaf images and constructs the convolution neural network model to detect tomato pests and diseases based on VGG16[8] and transfer...
Nowadays, merchandising is one of the significant method which allows to increase the sales. Therefore, activities such as monitoring the number of products on the shelves, completing the missing products and matching the planogram continuously have become important. An autonomous system is needed to automate operations such as product or brand recognition, stock tracking and planogram matching. In...
In this paper, we introduce seven emotions and positive and negative emotion recognition methods using facial images and the development of apps based on the method. In previous researches, they used the deep-learning technology to generate models with emotion-based facial expressions to recognized emotions. There are existing apps that express six emotions, but not seven emotions and positive and...
This work focuses on cost reduction methods, applied on forest species recognition systems as a case-study. Current state-of-the-art shows that the accuracy of these systems, generally employing texture recognition approaches, have increased considerably in the past years. However, the cost in time to perform the recognition of input samples has also increased proportionally. By taking into account...
This paper proposes a novel hybrid model that integrates the synergy of two superior classifiers for functional magnetic resonance imaging (fMRI) recognition, namely, convolutional neural networks (CNNs) and support vector machines (SVMs), both of which have proven results in the field of image recognition. In the proposed model, the CNN functions as a trainable feature extractor and the SVM functions...
The paper presents an application of transfer learning using convolutional neural network (CNN) in recognition of the drill state on the basis of hole images drilled in the laminated chipboard. Three classes are recognized: red, yellow and green, which correspond with 3 stages of drill state. Red class indicates the drill, which is worn out and should be replaced immediately in drilling process. Yellow...
Plant recognition from their leaves has become a popular area in the machine learning and image processing. In this study 7 different types of apricot trees were determined and classified by using their leaves. At first leaves images were pre-processed. After than each image was scanned by 5×5 overlapping filter and median values of each filter process were recorded to represent the leaves. After...
Scene analysis is an integral part of autonomous vehicle. Information such as traffic sign boards information, Toll information, Vehicle Number Plate information etc., from the scene is helpful in analyzing the current location. Vision based techniques are critical for a given scene analysis and happens to be an important area of research, today. This paper present localizing and recognizing text...
Building recognition from images is a challenging task since pictures can be taken from different angles and under different illumination conditions. Most of the building recognition methods use local and global handcrafted image features and do not consider the rejection scenario, where the method have to be capable of identifying if a given image does not belong to any of the classes of interest...
The artificial visual detection and recognition of bridges' cracks bear great dangers, therefore, we put forward a method of digital and intelligent detection of bridges fractures, combined with machine vision and the Deep Belief Network technologies. This method adopts Raspberry Pi to collect and pre-process images, to transmit images data by the GPRS / 3G or wired networks. And it uses high-level...
We address the problem of using computer vision based techniques to collect high-level traffic parameters in dynamic, real-world environments. This is a challenging problem that has not been sufficiently studied. Specifically, with a focus on freight vehicles, we propose a comprehensive framework for monitoring freight traffic movements at any well-defined freight traffic generator, using a network...
Perceptual image of a product plays a significant role in decision making when users choose a product whose basic function is homogeneous nowadays. Designers try to design products that meet the all kinds of demands of users. However, a big gap between designers and users exists owning to the subjectivity of designers' experience. An objective model to recognize perceptual image of products is proposed...
There are numerous potential applications for the Internet of Things (IOT) at the present stage. In the topic of image processing, the gender recognition usually adopts face information to identify the gender of a person. Therefore, if the input image loses face information, it will result in the wrong identification result. In this paper, we proposed a multiple-attributes (MA) recognition method...
As the issue of robustness of face recognition based on depth image sets, we propose that multiple Kinect images is being as a set of images, and depth data captured is used to automatically estimate poses and crop face area. Firstly, divide image sets into c subsets, and divide the images in all the subsets into image blocks of 4×4. Then, simulate images in sets as a form of image blocks, dividing...
The study of flower classification system is a very important subject in the field of Botany. A classifier of flowers with high accuracy will also bring a lot of fun to people's lives. However, because of the complex background of flowers, the similarity between the different species of flowers, and the differences among the same species of flowers, there are still some challenges in the recognition...
A facial expression is exhibited by the movement of muscles underneath the face skin. Automatic Facial Expression Recognition comprises of three main phases: Feature Extraction, Feature Selection and Expression Classification. Facial Expression Recognition (FER) has a very important role in computer vision, human machine interaction and modern gaming. The objective of this research work is to explore...
Sparse representation based classification (SRC) has been introduced as a new algorithm for face recognition classification instead of the classical gradient-based algorithms. However, there are some limitations that influence the robustness properties in SRC. One of the most effective parameters that impacts the SRC performance is the directory of training samples. It should contain enough samples...
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