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To solve the problem of training rate decline in neural network caused by too much noise in the traditional image, a new method of expression recognition based on CNN was proposed. First, in order to narrow the face range, face image could be detected from the original image by using the AdaBoost cascade classifier. Then, the coordinates of the eye, mouth and other key parts and brow, nasolabial and...
Gender recognition from face images is a challenging problem with applications in various knowledge domains, such as biometrics, security and surveillance, human-computer interaction, among others. In this work, we propose and evaluate a novel method for gender recognition based on a geometric descriptor constructed from a pre-defined face shape model. The proposed approach, tested on four different...
Herbal medicines can be used in treating health conditions under qualified medical observation. Various advantages are perceived by many consumers as being associated with using herbal medicines compared to conventional pharmaceutical products, such as the reduced risk of side effects, effectiveness with chronic conditions, lower cost and large availability, although robust evidence supporting such...
Pollen granules are one of the most stable microstructures of herbal flowers, and their ektexines possess strong anti-acid, anti-base and anti-biolysis properties. Therefore, the microstructures of pollen granules are not destroyed during storage, manufacturing and the production of different preparations. The shapes, sizes and colors of pollen granules are different in different families, genera...
Nowadays, image processing is getting more popular due to the daily increase of diverse data acquisition methods such as digital scanners and cameras. Due to the high volume of archived documents, automatic document classification methods can help to save the time and space in digital document organization. Logos in official and business documents are used to identify document identities. Different...
Plants play an important role in Earth's ecology by providing sustenance, shelter and maintaining a healthy atmosphere. Some of these plants have important medicinal properties. Automatic recognition of plant leaf is a challenging problem in the area of computer vision. An efficient Ayurvedic plant leaf recognition system will beneficial to many sectors of society which include medicinal field botanic...
This paper presents a methodology for recognition of handwritten Marathi and English Characters-Numerals using shape context descriptor. During pre-processing an algorithm is developed to extract the Marathi and English Characters-Numerals form grid formatted datasheets. The corresponding sample points around the boundary of a character are computed. This is followed by obtaining the centroid of the...
Bag-of-features (BoF) shows a great power in representing images for image classification. Many codebook learning methods have been developed to find discriminative parts of images for fine-grained recognition. Built upon BoF framework, we propose a novel approach for finegrained fish recognition with two-level codebook learning by shrinking coding coefficients. In the framework, only the maximum-valued...
This work introduces a novel artificial intelligence approach to household object recognition. The approach used in this work is feature-based and it works toward recognition under a broad range of circumstances. The necessary image processing techniques are applied to recognize the objects. These techniques include removal of shadow that is segmenting the object from its shadow, extraction of shape...
Automatically recognizing pornographic images from the Web is a vital step to purify Internet environment. Inspired by the rapid developments of deep learning models, we present a deep architecture of convolutional neural network (CNN) for high accuracy pornographic image recognition. The proposed architecture is built upon existing CNNs which accepts input images of different sizes and incorporates...
Gender recognition from face images is an important application in the fields of security, retail advertising and marketing. We propose a novel descriptor based on COSFIRE filters for gender recognition. A COSFIRE filter is trainable, in that its selectivity is determined in an automatic configuration process that analyses a given prototype pattern of interest. We demonstrate the effectiveness of...
Facial expression recognition has become key challenge in the field of anthropomorphic human-computer interaction. In this paper, an approach is presented for facial expression recognition through the shape of facial feature points and the texture information of specific areas, based on Active Appearance Model (AAM). First, find out that the shape and texture parameters can express more personalized...
This paper proposes a methodology for recognition of plant species by using a set of statistical features obtained from digital leaf images. As the features are sensitive to geometric transformations of the leaf image, a pre processing step is initially performed to make the features invariant to transformations like translation, rotation and scaling. Images are classified to 32 pre-defined classes...
Recent development in depth sensors opens up new challenging task in the field of computer vision research areas, including human-computer interaction, computer games and surveillance systems. This paper addresses shape and motion features approach to observe, track and recognize human silhouettes using a sequence of RGB-D images. Under our proposed activity recognition framework, the required procedure...
In general, it is difficult to construct an object recognition system, because such a system has many design variables and often these cannot be designed independently. However, in certain manufacturing tasks, it is not always necessary to design all variables. In this study, we selected a picking task as the target task for the experiment. We restricted the design variables to parameters of the preprocessing...
Many research studies demonstrated that recognition based on ear biometrics offers an accuracy which is comparable to face trait, especially in controlled settings. Our proposal is to exploit it to avoid the problem of newborn swap, which is possible and actually happens, most of all in crowded maternity wards of big hospitals. We tested the viability of this solution using a dataset of ear images...
Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, we conduct a comparative study on leaf image recognition and propose a novel learning-based leaf image recognition technique via sparse representation (or sparse coding) for automatic plant identification...
Within the framework of a smartphone-based application, helping people to identify plant species in the wild, a sub-classifier strategy has been introduced. It aims at recognizing the botanical properties of a leaf, relatively to various global and local shape criteria used in flora books. A decision function is applied on these classified shape categories to produce a final decision on the species...
We propose a novel approach for learning image representation based on qualitative assessments of visual aesthetics. It relies on a multi-node multi-state model that represents image attributes and their relations. The model is learnt from pair wise image preferences provided by annotators. To demonstrate the effectiveness we apply our approach to fashion image rating, i.e., comparative assessment...
Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, a novel leaf image recognition technique via sparse representation is proposed for automatic plant identification. In order to model leaf images, we learn an overcomplete dictionary for sparsely representing...
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