Tables of integrals

This paper proposes a novel technique for fruit detection in natural environments which is applicable in automatic harvesting robots, yield estimation systems and quality monitoring systems. As most color-based techniques are highly sensitive to illumination changes and low contrasts between fruits and leaves, the proposed technique, conversely, is based on contour information. Firstly, a discriminative shape descriptor is derived to represent geometrical properties of arbitrary fragment, and applied to a bidirectional partial shape matching to detect sub-fragments of interest that match parts of a reference contour. Then, a novel probabilistic Hough transform is developed to aggregate these sub-fragments for obtaining fruit candidates. Finally, all fruit candidates are verified by a support vector machine classifier trained on color and texture features. Citrus, tomato, pumpkin, bitter gourd, towel gourd and mango datasets were provided. Experiments on these datasets demonstrated that the proposed approach was competitive for detecting most type of fruits, such as green, orange, circular and non-circular, in natural environments.

Fruit detection in natural environment using partial shape matching and probabilistic Hough transform

With advances in machine vision systems (e.g., artificial eye, unmanned aerial vehicles, surveillance monitoring) scene semantic recognition (SSR) technology has attracted much attention due to its related applications such as autonomous driving, tourist navigation, intelligent traffic and remote aerial sensing. Although tremendous progress has been made in visual interpretation, several challenges remain (i.e., dynamic backgrounds, occlusion, lack of labeled data, changes in illumination, direction, and size). Therefore, we have proposed a novel SSR framework that intelligently segments the locations of objects, generates a novel Bag of Features, and recognizes scenes via Maximum Entropy. First, denoising and smoothing are applied on scene data. Second, modified Fuzzy C-Means integrates with super-pixels and Random Forest for the segmentation of objects. Third, these segmented objects are used to extract a novel Bag of Features that concatenate different blobs, multiple orientations, Fourier transform and geometrical points over the objects. An Artificial Neural Network recognizes the multiple objects using the different patterns of objects. Finally, labels are estimated via Maximum Entropy model. During experimental evaluation, our proposed system illustrated a remarkable mean accuracy rate of 90.07% over the MSRC dataset and 89.26% over the Caltech 101 for object recognition, and 93.53% over the Pascal-VOC12 dataset for scene recognition, respectively. The proposed system should be applicable to various emerging technologies, such as augmented reality, to represent the real-world environment for military training and engineering design, as well as for entertainment, artificial eyes for visually impaired people and traffic monitoring to avoid congestion or road accidents.

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Scene Semantic Recognition Based on Modified Fuzzy C-Mean and Maximum Entropy Using Object-to-Object Relations

Object recognition is one of the research area in the field of computer vision and image processing because of its varied applications in surveillance and security systems, biometrics, intelligent vehicle system, content based image retrieval, etc. Many researchers have already done a lot of work in this area, but still there are many issues like scale, rotation, illumination, viewpoint, occlusion, background clutter among many more that draw the attention of the researchers. Object recognition is the task of recognizing the object and labeling the object in an image. The main goal of this survey is to present a comprehensive study in the field of 2D object recognition. An object is recognized by extracting the features of object like color of the object, texture of the object or shape or some other features. Then based on these features, objects are classified into various classes and each class is assigned a name. In this paper, various feature extraction techniques and classification algorithms are discussed which are required for object recognition. As the deep learning has made a tremendous improvement in object recognition process, so the paper also presents the recognition results achieved with various deep learning methods. The survey also includes the applications of object recognition system and various challenges faced while recognizing the object. Pros and cons of feature extraction and classification algorithms are also discussed which may help other researchers during their initial period of study. In this survey, the authors have also reported an analysis of various researches that describes the techniques used for object recognition with the accuracy achieved on particular image dataset. Finally, this paper ends with concluding notes and future directions. The aim of this study is to introduce the researchers about various techniques used for object recognition system.

2D Object Recognition Techniques: State-of-the-Art Work

Shape information plays an important role in the human visual system, and how to represent the shape accurately is a very challenging issue in shape recognition. In recent years, many shape description approaches have been proposed. However, most of the existing methods are still facing challenges on accuracy and computational efficiency. Fourier descriptor is regarded as a promising shape description method because it has a solid theoretical foundation and at the same time possesses the strength of appealing invariance properties and high computational efficiency. We introduce a novel multiscale Fourier descriptor based on triangular features which is used to identify shapes. The local and global characteristics of a shape are effectively captured by the proposed shape descriptor. Meanwhile, the proposed shape descriptor has good properties, such as the invariant of the geometric transformation and the starting point of an object. We tested this descriptor on four popular benchmarking datasets, including MPEG-7, Swedish leaf, ETH-80, and Flavia leaf. The results confirm that our method is better than comparable-complexity approaches based on Fourier descriptors and does not perform unfavorably with respect to more complex, state-of-the-art, shape descriptors. In particular, our method is far superior to the complex shape description methods in terms of retrieval efficiency and computational complexity.

Multiscale Fourier descriptor based on triangular features for shape retrieval

Shape-based object recognition via Evidence Accumulation Inference

Many state-of-the-art shape features have been proposed for the shape recognition task. In this paper, to explore whether a shape feature influences object segmentation, we propose a specific shape feature, Fisher shape (a form of bag of contour fragments), and we combine this with the appearance feature with multiple kernel learning to create a pipeline of object segmentation system. The experimental results on benchmark datasets clearly demonstrate that the pipeline of object segmentation is effective and that the Fisher shape can improve object segmentation with only the appearance feature.

Bag of contour fragments for improvement of object segmentation

Plant identification based on leaf images is a widely concerned application field in artificial intelligence and botany. The key problem is extracting robust discriminative features from leaf images and assigning a measure of similarity. This study proposes an effective, robust shape descriptor to identify plant species from images of their leaves, which we call the high-level triangle shape descriptor (HTSD). First, we extract a leaf image’s external contour and internal salient point information. We then use triangle features to describe the leaf contour, which we call the contour point based on triangle features (CPTFs). The internal information of the leaf image is based on salient point triangle features (SPTFs). The third step is to apply the Fisher vector to encode the two kinds of point-based local triangle features into the HTSD. Finally, we employ the simple euclidean distance to calculate the dissimilarities between the HTSD characteristics of leaf images. We have extensively evaluated the proposed approach on several public leaf datasets successfully. Experimental results show that our method has superior recognition accuracy, outperforming current state-of-the-art shape-based and deep-learning plant identification approaches.

A Learning Robust and Discriminative Shape Descriptor for Plant Species Identification

Plant leaf identification is an important and challenging issue in botany and image analysis. This is because leaves of the same class exhibit very large differences, and leaves of different classes have very small changes. In this article, we propose a novel plant identification method by combining shape and convolutional characteristics. First, we present an effective shape descriptor named improved multiscale triangle descriptor (IMTD) to capture the shape properties of a leaf. Then, we analyze different levels of convolutional features for plant leaf identification. Finally, we combine complementary shape and convolutional features for the task of plant leaf recognition. The proposed method has been tested on nine benchmark leaf datasets, including the Flavia, CVIP100, Swedish, MEW2012, ICL Compound, LeafSnap, Mulberry, Corn leaf disease, and Turkey-Plant dataset. Our method achieves good recognition results on nine benchmark leaf datasets for the unsupervised leaf image retrieval and supervised leaf image classification. The recognition performance of our approach is better than prior state-of-the-art plant identification approaches. 

Plant leaf identification based on shape and convolutional features

Fine-grained cultivar recognition has recently attracted considerable attention from researchers in pattern recognition and botany. However, this problem is highly challenging because the differences between cultivated species are so subtle that it is difficult to distinguish them effectively. This article presents a novel deep convolutional feature aggregation approach for fine-grained cultivar recognition. First, we propose a description method of the regional convolution covariance feature (RCCF), which describes the subtle changes in cultivated species by accumulating low-level convolution features and has a strong discriminating ability. Second, we also improve the regional maximum activation of convolutions (RMAC) and present a multiresolution RMAC high-level convolutional feature. Finally, we combine complementary RCCF with multiresolution RMAC features for fine-grained cultivar recognition, which significantly improves the recognition accuracy of fine-grained cultivated plants. We have carried out extensive tests on four benchmark cultivar plant datasets. The results show that our approach achieves state-of-the-art recognition performance on four benchmark cultivar plant datasets, surpassing other plant species identification methods. 

Qian Yu

Papers

Shape-based object recognition via Evidence Accumulation Inference

Bag of contour fragments for improvement of object segmentation

A Learning Robust and Discriminative Shape Descriptor for Plant Species Identification

Plant leaf identification based on shape and convolutional features

Deep convolutional feature aggregation for fine-grained cultivar recognition