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The performance of pattern classifiers depends on the separability of the classes in the feature space — a property related to the quality of the descriptors — and the choice of informative training samples for user labeling — a procedure that usually requires active learning. This work is devoted to improve the quality of the descriptors when samples are superpixels from remote sensing images. We...
In the article a vision system for shape and colour recognition of dishes (plates, bowls, mugs), which can be used to automate the process of customer service in a self-service canteen is described. In consists of three basic components: object segmentation using so-called background model subtraction, shape recognition using geometric invariant moments and SVM classifier, as well as colour recognition...
Plants are fundamental for human beings, so it's very important to catalog and preserve all the plants species. Identifying an unknown plant species is not a simple task. Automatic image processing techniques based on leaves recognition can help to find the best features useful for plant representation and classification. Many methods present in literature use only a small and complex set of features,...
In this paper we explore ways to address the issue of dataset bias in person re-identification by using data augmentation to increase the variability of the available datasets, and we introduce a novel data augmentation method for re-identification based on changing the image background. We show that use of data augmentation can improve the cross-dataset generalisation of convolutional network based...
We propose a novel approach to segment hand regions in egocentric video that requires no manual labeling of training samples. The user wearing a head-mounted camera is prompted to perform a simple gesture during an initial calibration step. A combination of color and motion analysis that exploits knowledge of the expected gesture is applied on the calibration video frames to automatically label hand...
In this paper, we examined the effectiveness of deep convolutional neural network (DCNN) for food photo recognition task. Food recognition is a kind of fine-grained visual recognition which is relatively harder problem than conventional image recognition. To tackle this problem, we sought the best combination of DCNN-related techniques such as pre-training with the large-scale ImageNet data, fine-tuning...
In creating web pages, books, or presentation slides, consistent use of tasteful visual style(s) is quite important. In this paper, we consider the problem of style-based comparison and retrieval of illustrations. In their pioneering work, Garces et al. [2] proposed an algorithm for comparing illustrative style. The algorithm uses supervised learning that relied on stylistic labels present in a training...
Recently, the sparse coding based image representation has achieved state-of-the-art recognition results on many benchmarks. In this paper, we propose Multi-cue Normalized Non-Negative Sparse Encoder (MN3SE) which enforces both the non-negative constraint and the shift-invariant constraint on top of the traditional sparse coding criteria, and takes multi-cue to further boost the performance. The former...
Detecting the text in natural scene images is often challenging due to the complexity and variety of text's appearance and its interaction with the scene context. In this paper, we present a novel hierarchical text detection method exploiting textual characteristics at both character and text line scales for improved accuracy. First, seed candidate characters are detected with discriminative deep...
This research proposes an Android application to estimate sambiloto (Andrographis paniculata) leaf's age from its estimated spectral reflectance using Wiener estimation. Sambiloto is one of Indonesia's popular medicinal plant. In order to use quality plants, a quality control method, such as lab tests, must be conducted. These lab tests require the destruction of leaf samples. One promising alternative...
This paper proposes a promising new approach to detect underwater threats in side scan sonar (SSS) images without machine learning procedure. Although object detection requires high reliability, the maritime environment changes unpredictably and dynamically. In order to accomplish high reliability for object detection systems, a huge number of the samples under various different environments are required...
Object recognition and pose estimation from RGB-D images are important tasks for manipulation robots which can be learned from examples. Creating and annotating datasets for learning is expensive, however. We address this problem with transfer learning from deep convolutional neural networks (CNN) that are pre-trained for image categorization and provide a rich, semantically meaningful feature set...
Face Detection is a challenging task due to large variations in pose, illumination, occlusion, scaling and clutter. Face detection is the primary step in Face recognition. The important goal of efficient face detection system is to have negligible misclassification rate. A novel face detection technique using CbCr color model with Haar feature extractor and Adaboost classifier is proposed. The proposed...
Content Based Video Classification is becoming necessary for various video analysis applications to be able to handle humongous amounts of video data being generated & shared all over the Internet. This paper proposes use of DTTBTC for color based feature extraction from video key frames which are used by machine learning classifiers for training & testing. Experimental results show accuracy...
Content based classification approach is becoming necessary to support the retrieval and indexing of images. This paper uses Color features of an image to form a feature vector on which data pre-processing is applied. These features are then used by machine learning classifiers to classify the images. Classification accuracy is evaluated in two color spaces and image sizes. Empirical results show...
With the exponential growth of storage of digital images, retrieval has become an impending issue. Such large collection of data takes a considerable amount of time in retrieving images apart from picking relevant images with respect to the query. Despite advancements in introducing effective features, the search time still remains larger. In such scenario the search time could be minimized by categorizing...
In order to utilize identification to the best extent, we need robust and fast algorithms and systems to process the data. Having palmprint as a reliable and unique characteristic of every person, we extract and use its features based on its geometry, lines and angles. There are countless ways to define measures for the recognition task. To analyze a new point of view, we extracted textural features...
This paper presents a real-time computer visionbased Bengali Sign Language (BdSL) recognition system. The system detects the probable hand from the captured image. The system uses Haar-like feature-based cascaded classifiers to detect the hand in each frame. From the detected hand area, the system extracts the hand sign based on Hue and Saturation value corresponding to human skin color. After normalization...
The aim of this paper is to develop an effective classification approach based on Random Forest (RF) algorithm. Three fruits; i.e., apples, Strawberry, and oranges were analysed and several features were extracted based on the fruits' shape, colour characteristics as well as Scale Invariant Feature Transform (SIFT). A preprocessing stages using image processing to prepare the fruit images dataset...
This paper presents the detection and localization methods of entrance and staircase markers for the team E-Mobile in TechX Challenge 2013. Autonomous vehicles are required to detect and locate traffic cones beside the indoor entrance and staircase. One big challenge is from the unpredictable lighting conditions and environment. Different practical techniques such as color space selection, segmentation,...
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