Contour-based Shape Descriptors Research Articles

Revealing leaf species through specific contour and region-based features extraction

Leaf identification plays a vital role in plant classification and protection, and advancements in artificial intelligence and machine vision have revolutionized leaf identification technology. This study focuses on evaluating different feature extraction methods, specifically region-based and contour-based approaches, to identify plant leaf species through image analysis. The study initially extracts leaf characteristics from images, which are then divided into two groups based on area features or contour features. The dataset is split into 80% for training and 20% for testing, and a K-Nearest Neighbour (K-NN) classifier is employed for species identification. Three approaches are compared for leaf classification. The first approach utilizes region-based shape descriptors (RBSD) and achieves an accuracy rate of 75%. The second approach employs contour-based shape descriptors (CBSD) and achieves a higher accuracy of 76%. The third approach combines both area and contour features, resulting in an average accuracy of 75.5%. Through the comparison of all three methods, it is evident that the CBSD approach outperforms the others, making it the most effective feature extraction method for plant species identification. The developed automatic leaf species identification system was implemented using the PyCharm environment.The proposed approach and feature extraction techniques surpass previous state-of-the-art 69.61%, 72%, 74.4% results, showcasing the potential of the proposed separation of leaf characteristics. Also the advancement of automated leaf species identification and have significant implications for plant taxonomy and protection efforts.

Enhanced Visual Analytics Technique for Content-Based Medical Image Retrieval

Content-based image retrieval (CBIR) is a method for searching that finds related images in a medical database. Furthermore, a clinical adaptation of CBIR is hampered in part by a contextual gap that is the disparity among the person characterization of the picture and the framework characterization of the image. This technique makes it tough for the user to validate the fetched images that are similar to the query image in addition to that it only fetches the images of top-ranked and ignores the low-ranking ones. Visual Analytics for Medical Image Retrieval is a novel procedure for medicinal CBIR proposed in this research (VAMIR). By integrating human and machine analysis, Visual Analytics provides the potential to address the above-mentioned significant challenges. The texture properties are retrieved using the shape features extraction and Gray Level Co-occurrence Matrix (GLCM) is performed by contour-based shape descriptor. Using the Euclidean distance correlation metric, related medical pictures will be fetched by distinguishing the query image's attribute vector with the database images' respective attribute vectors. A vector of multiple features outperforms a vector of a single feature in terms of quality. The VAMIR implementation demonstrates that the search outcome for the user is acquired with 90% of recall and precision.

Fish Shape Recognition using Multiple Shape Descriptors

This paper studies recognition of fish shapes using both Region based and Contour based shape based descriptors[9]. Moment Invariants are chosen as the Region based descriptor and the Simple (geometric) shape descriptors (SSD) are used as Contour based shape descriptors. The shapes are varied through scaling and rotation. Manhattan Distance is used as the classifier. The study of the recognition rate by using moment invariants and simple shape descriptors is done separately. Each moment invariant (M1 , M2 , M3 , M4 and M5) is studied separately and jointly. Then simple shape descriptors are combined with moment invariants to get hybrid feature vectors for improving recognition rate. General Terms Fish Shape recognition

An efficient contour based shape descriptor for sanitation network pipes digital images

An efficient contour based shape descriptor for sanitation network pipes digital images

Multiscale Distance Matrix for Fast Plant Leaf Recognition

In this brief, we propose a novel contour-based shape descriptor, called the multiscale distance matrix, to capture the shape geometry while being invariant to translation, rotation, scaling, and bilateral symmetry. The descriptor is further combined with a dimensionality reduction to improve its discriminative power. The proposed method avoids the time-consuming pointwise matching encountered in most of the previously used shape recognition algorithms. It is therefore fast and suitable for real-time applications. We applied the proposed method to the task of plan leaf recognition with experiments on two data sets, the Swedish Leaf data set and the ICL Leaf data set. The experimental results clearly demonstrate the effectiveness and efficiency of the proposed descriptor.

A cognitive evaluation procedure for contour based shape descriptors

Present image processing algorithms are unable to extract a neat and closed contour of an object of interest from a natural image. Advanced contour detection algorithms extract the contour of an object of interest from a natural scene with a side effect of depletion of the contour. Hence in order to perform well in a real world scenario, object recognition algorithms should be robust to contour incompleteness. With inspiration from psychophysical studies of the human cognitive abilities we propose a novel method to evaluate the performance of object recognition algorithms in terms of their robustness to incomplete contour representations. Complete contour representations of objects are used as a reference (training) set. Incomplete contour representations of the same objects are used as a test set. The performance of an algorithm is evaluated using the recognition rate as a function of the percentage of contour retained. The test framework is illustrated by using two contour based shape recognition algorithms which use a shape context and a distance multiset as shape descriptors. Three types of contour incompleteness, viz. segment-wise contour deletion, occlusion and random pixel depletion, are considered. In our experiments we use images from the COIL and MPEG-7 datasets. Both algorithms qualitatively perform similar to the human visual system in the sense that recognition performance monotonously increases with the degree of completeness and that they perform best in the case of random depletion and worst in the case of occluded contours. The distance multiset shape descriptor outperforms the shape context in this test especially for high levels of incompleteness.

Special issue: Recent developments in hybrid intelligent systems

The human-centric facet of intelligent systems, which are inherently based upon the principle of hybridization of the individual information technologies, is definitely an interesting and highly promising avenue to follow. The diversity of communication channels between information systems and the environment is immense, and their effective realization does call for a truly heterogeneous nature of the underlying technologies and algorithmic frameworks. Consider, for instance, processing visual information, which became omnipresent in many applications including a broad range of inspection and manufacturing systems. Computer vision offered remarkable opportunities of processing therein. Fuzzy sets support a granular view at images and form a setting for their interpretation at the higher, more abstract level. Neural networks and evolutionary optimization provide badly needed learning abilities. The technologies mentioned so far, when combined into an inherently hybrid system, help address the needs of intelligent vision systems and build a coherent and versatile implementation. Managing knowledge at various levels, starting from the detailed pixel-level and moving to the far more general abstract level, becomes crucial to the success of such systems. This special issue is a testimony to the ongoing innovation and recent developments in hybrid information systems. The study by Shahram Jafari and Ray Jarvis focuses on implementing perception in the context of robotic eye-to-hand coordination. The authors show an integration of different novel concepts to perform scene analysis, hand-eye coordination and object manipulation for robotic tasks. The underlying architectural considerations rely on the usage of the neuro-fuzzy systems in the formation of scene analysis and object recognition. Next, Ghosh and Petkov, in their study, deal with the cognitive evaluation of contour based shape descriptors in image processing. In the sequel, first-order logical networks discussed by Kijsirikul and Lerdlamnaochai offer an interesting insight into the problems of logic-based neurocomputing. The proposed method, called First-Order Logical Neural Network (FOLNN), leads to hybrid structures that combine standard feed-forward neural networks and mechanisms of inductive learning. The study by Sehgal, Gondal, and Dooley is focused on an important and timely issue of handling multiple missing values of genetic expression in problems of analysis and classification of microarray data. The authors offer a mechanism of a k-ranked covariance-based missing value imputation and demonstrate its superiority over the k-nearest neighbor method.

Evaluation of MPEG-7 shape descriptors against other shape descriptors

Shape is an important image feature-it is one of the primary low level image features exploited in content-based image retrieval (CBIR). There are generally two types of shape descriptors in the literature: contour-based and region-based. In MPEG-7, the curvature scale space descriptor (CSSD) and Zernike moment descriptor (ZMD) have been adopted as the contour-based shape descriptor and region-based shape descriptor, respectively. In this paper, the two shape descriptors are evaluated against other shape descriptors, and the two shape descriptors are also evaluated against each other. Standard methodology is used in the evaluation. Specifically, we use standard databases, large data sets and query sets, commonly used performance measurement and guided principles. A Java-based client-server retrieval framework has been implemented to facilitate the evaluation. Results show that Fourier descriptor (FD) outperforms CSSD, and that CSSD can be replaced by either FD or ZMD.

A comparative study of curvature scale space and Fourier descriptors for shape-based image retrieval

Contour shape descriptors are among the important shape description methods. Fourier descriptors (FDs) and curvature scale space descriptors (CSSDs) are widely used as contour shape descriptors for image retrieval in the literature. In MPEG-7, CSSD has been proposed as one of the contour-based shape descriptors. However, no comprehensive comparison has been made between these two shape descriptors. In this paper we study and compare FD and CSSD using standard principles and standard database. The study targets image retrieval application. Our experimental results show that FD outperforms CSSD in terms of robustness, low computation, hierarchical representation, retrieval performance, and suitability for efficient indexing.

Object-based video abstraction for video surveillance systems

Key frames are the subset of still images which best represent the content of a video sequence in an abstracted manner. In other words, video abstraction transforms an entire video clip to a small number of representative images. We present a scheme for object-based video abstraction facilitated by an efficient video-object segmentation (VOS) system. In such a framework, the concept of a "key frame" is replaced by that of a "key video-object plane (VOP)." In order to achieve an online object-based framework such as an object-based video surveillance system, it becomes essential that semantically meaningful video objects are directly accessed from video sequences. Moreover, the extraction of key VOPs needs to be automated and context dependent so that they maintain the important contents of the video while removing all redundancies. Once a VOP is extracted, the shape of the VOP needs to be well described. To this end, both region-based and contour-based shape descriptors are investigated, and the region-based descriptor is selected for the proposed system. The key VOPs are extracted in a sequential manner by successive comparison with the previously declared key VOP. Experimental results on the proposed online processing scheme combined with efficient VOS show the proposed integrated scheme generates desirable summarizations of surveillance videos.

MPEG-7 visual shape descriptors

This paper describes techniques and tools for shape representation and matching, developed in the context of MPEG-7 standardization. The application domains for each descriptor are considered, and the contour-based shape descriptor is presented in some detail. Example applications are also shown.