Modifications of a sinarback 54 digital camera for spectral and high-accuracy colorimetric imaging: simulations and experiments

A search technique was used to identify sets of colored glass filters that could be placed in the optical path of the Sinarback 54 camera system resulting in improved color accuracy compared with a production unit and the ability to perform spectral estimation. A green and blue filter, each a pair of filters, were identified and constructed from Schott glass. RGB images were collected through these two filters resulting in six image planes. Using the Gretag Macbeth ColorChecker DC and a custom target of blue artist pigments, a transformation was derived that converted digitally flat-fielded and photometrically-linearized camera signals to estimated spectral reflectance factor. The combination of using these two filter “sandwiches” and appropriate mathematics resulted in more than a twofold improvement in color and spectral accuracy compared with the production camera. The average colorimetric and spectral performance is shown in the following bar graphs for the calibration targets and independent-verification targets, the Esser TE221 test chart, a custom target of artist pigments made using the Gamblin Conservation Colors, and the traditional GretagMacbeth ColorChecker Color Rendition chart. These results indicate that it is possible to achieve excellent color accuracy and acceptable spectral accuracy using a color-filter array sensor

Object Tracking from Unstabilized Platforms by Particle Filtering with Embedded Camera Ego Motion

Visual tracking with moving cameras is a challenging task. The global motion induced by the moving camera moves the target object outside the expected search area, according to the object dynamics. The typical approach is to use a registration algorithm to compensate the camera motion. However, in situations involving several moving objects, and backgrounds highly affected by the aperture problem, image registration quality may be very low, decreasing dramatically the performance of the tracking. In this work, a novel approach is proposed to successfully tackle the tracking with moving cameras in complex situations, which involve several independent moving objects. The key idea is to compute several hypotheses for the camera motion, instead of estimating deterministically only one. These hypotheses are combined with the object dynamics in a Particle Filter framework to predict the most probable object locations. Then, each hypothetical object location is evaluated by the measurement model using a spatiogram, which is a region descriptor based on color and spatial distributions. Experimental results show that the proposed strategy allows to accurately track an object in complex situations affected by strong ego motion

Temu Kembali Citra Berbasis Isi Pada Citra Kain Berdasrkan Fitur Warna, Tekstur, Dan Bentuk

Kain adalah bahan baku pembuatan berbagai produk, seperti seprai, taplak, dan baju. Kain memiliki berbagai jenis bahan, warna, dan motif. Terkadang, beberapa kain berbeda dipadukan untuk membuat suatu produk, misalnya baju. Tidak sembarang kain dipilih. Warna yang senada ataupun motif yang mirip biasanya menjadi syarat kain tersebut dipadukan dengan kain contoh. Untuk mempermudah pencarian kain yang mirip dengan kain contoh, content-based image retrieval (CBIR) dapat menjadi salah satu solusi. CBIR dapat menemukan kain yang mirip sehingga membantu pemilihan kain yang akan dipadukan dengan kain contoh. Dalam Tugas Akhir ini, dibangun sistem temu kembali citra kain menggunakan metode dominant color descriptor (DCD), steerable filter, dan pseudo-Zernike moments. DCD digunakan untuk mengekstraksi fitur warna dari citra yang warnanya telah dikuantisasi. Steerable filter digunakan untuk mengekstraksi fitur tekstur. Pseudo-Zernike moment digunakan untuk mengekstraksi fitur bentuk. Hasil pencarian citra mirip yang paling baik didapatkan dengan menggabungkan ketiga fitur, yaitu warna, tekstur, dan bentuk. Bobot dari tiap fitur berbeda, yaitu 0.6 untuk fitur tekstur, 0.25 untuk fitur warna, dan 0.15 untuk fitur bentuk. Apabila hanya digunakan satu fitur, yang paling baik digunakan untuk pencarian adalah fitur warna. ============================================================================================================================== Fabric is the raw material for making various products, such as bedsheet, tablecloth, and clothes. Fabrics have different types of materials, colors, and motif. Sometimes, more than one different fabrics combine to create a product, such as clothes. The selected fabrics are chosen by similarity. Matching in color or motif is usually a requirement to combined some fabrics. To facilitate the search of similar fabric, content-based image retrieval (CBIR) can be the solution. CBIR can find the similar fabric to help the selection of fabric that will be combine with another fabric. In this Final Project, image retrieval system is constructed using dominant color descriptor (DCD), a steerable filter, and pseudo-Zernike moments. DCD is used to extract color features of quantized colored image. Steerable filter is used to extract texture features. Pseudo-Zernike moments are used to extract the shape feature. The best result of image retrieval obtained by combining three features, color, texture, and shape. The weight of each features are different, 0.6 for texture features, 0.25 for color features, and 0.15 for shape features. Meanwhile, when only one feature is selected, color feature is the best used for the search

Accessibility-based reranking in multimedia search engines

Traditional multimedia search engines retrieve results based mostly on the query submitted by the user, or using a log of previous searches to provide personalized results, while not considering the accessibility of the results for users with vision or other types of impairments. In this paper, a novel approach is presented which incorporates the accessibility of images for users with various vision impairments, such as color blindness, cataract and glaucoma, in order to rerank the results of an image search engine. The accessibility of individual images is measured through the use of vision simulation filters. Multi-objective optimization techniques utilizing the image accessibility scores are used to handle users with multiple vision impairments, while the impairment profile of a specific user is used to select one from the Pareto-optimal solutions. The proposed approach has been tested with two image datasets, using both simulated and real impaired users, and the results verify its applicability. Although the proposed method has been used for vision accessibility-based reranking, it can also be extended for other types of personalization context

Analisis dan Implementasi CBIR Menggunakan Penggabungan Variansi Ciri Warna dan Bentuk

ABSTRAKSI: Image Retrieval merupakan sebuah bidang ilmu untuk melakukan pencarian terhadap suatu gambar. Ada 2 cara untuk melakukan pencarian gambar, yaitu context based, pencarian dilakukan menggunakan meta data, dan content based, pencarian dilakukan menggunakan content dari gambar tersebut. Content dari gambar merupakan fitur-fitur yang terdapat pada file gambar, berupa warna, bentuk, dan tekstur.Dalam tugas akhir ini, sistem image retrieval dibangun menggunakan 2 fitur, yaitu fitur warna dan bentuk, dengan pencocokkan berbasis histogram. Pencocokkan berbasis histogram sangat dipengaruhi oleh cara bagaimana fitur diekstraksi dan direpresentasikan dalam histogram serta metode perhitungan similarity yang digunakan. Fitur warna diekstraksi menggunakan model RGB atau HSV sedangkan ekstraksi fitur bentuk bergantung bagaimana preprocessing gambar yang dilakukan sehingga tepi-tepi terdeteksi.Penggunaan model warna RGB dalam representasi satu histogram tergabung dan jumlah level kuantisasi sebesar 64 merupakan teknik terbaik dalam melakukan ekstraksi ciri warna dengan tingkat performansi nDCG sebesar 0.835. Untuk ekstraksi fitur bentuk penggunaan hysteria threshold dalam ekstraksi ciri dengan representasi 36 level (1 level= 5 derajat) dan besar nilai indeks bias 0.6 menghasilkan performansi nDCG sebesar 0.693. Performansi system untuk penggabungan fitur warna dan bentuk ditentukan pada pengaturan bobot 6 untuk warna dan 4 untuk bentuk dengan performansi nDCG yang dihasilkan 0.895Kata Kunci : Content Based Image Retrieval (CBIR), Histogram, Gauss filter, Bin, Similarity, RGB, HSV, tressholdABSTRACT: Image Retrieval is a field to search and retrieve information from an image. There are 2 methodes to search images, they are context based and content based. The context based retrieval uses metadata of the image. The content of image file are the features that represent the image such as color, shape, and texture feature.In this final project, image retrieval system has built using color and shape feature with histogram based extraction. Histogram matching based methode performance so depend on how the features are extracted and represented in histogram form. As the examples, color feature can be extracted by using RGB or HSV model color. And shape feature performance depend on how the preprocessing is done so the edges can be detected.RGB color model showed in 1 combination histogram with 64 quantitation levels produces good performance in accuracy and response with nDCG 0.835. For shape extraction, hysteria tresshold with σ = 0,6 has better results than single tresshold, with nDCG 0.693. And, integration of shape and color feature using WC (Weight Color) = 6 and WS (Weight Shape) can improve sistem performance with nDCG 0.895.Keyword: Content Based Image Retrieval, Histogram, Gauss filter, Bin, Similarity, RGB, HSV, tresshol

Generalized Kernel-based Visual Tracking

In this work we generalize the plain MS trackers and attempt to overcome standard mean shift trackers' two limitations. It is well known that modeling and maintaining a representation of a target object is an important component of a successful visual tracker. However, little work has been done on building a robust template model for kernel-based MS tracking. In contrast to building a template from a single frame, we train a robust object representation model from a large amount of data. Tracking is viewed as a binary classification problem, and a discriminative classification rule is learned to distinguish between the object and background. We adopt a support vector machine (SVM) for training. The tracker is then implemented by maximizing the classification score. An iterative optimization scheme very similar to MS is derived for this purpose.Comment: 12 page

Real time hand gesture recognition including hand segmentation and tracking

In this paper we present a system that performs automatic gesture recognition. The system consists of two main components: (i) A unified technique for segmentation and tracking of face and hands using a skin detection algorithm along with handling occlusion between skin objects to keep track of the status of the occluded parts. This is realized by combining 3 useful features, namely, color, motion and position. (ii) A static and dynamic gesture recognition system. Static gesture recognition is achieved using a robust hand shape classification, based on PCA subspaces, that is invariant to scale along with small translation and rotation transformations. Combining hand shape classification with position information and using DHMMs allows us to accomplish dynamic gesture recognition

Integration of Exploration and Search: A Case Study of the M3 Model

International audienceEffective support for multimedia analytics applications requires exploration and search to be integrated seamlessly into a single interaction model. Media metadata can be seen as defining a multidimensional media space, casting multimedia analytics tasks as exploration, manipulation and augmentation of that space. We present an initial case study of integrating exploration and search within this multidimensional media space. We extend the M3 model, initially proposed as a pure exploration tool, and show that it can be elegantly extended to allow searching within an exploration context and exploring within a search context. We then evaluate the suitability of relational database management systems, as representatives of today’s data management technologies, for implementing the extended M3 model. Based on our results, we finally propose some research directions for scalability of multimedia analytics