Abstract

Surface enhanced Raman scattering (SERS) spectra of 4-mercaptobenzoic acid (4-MBA) self-assembled monolayers (SAMs) on gold substrates is presented for SAMs onto which gold nanoparticles of various shapes have been electrostatically immobilized. SERS spectra of 4-MBA SAMs are enhanced in the presence of immobilized gold nanocrystals by a factor of 10 7-10 9 relative to 4-MBA in solution. Large enhancement factors are a likely result of plasmon coupling between the nanoparticles (localized surface plasmon) and the smooth gold substrate (surface plasmon polariton), creating large localized electromagnetic fields at their interface, where 4-MBA molecules reside in this sandwich architecture. Moreover, enhancement factors depend on nanoparticle shape, and vary by a factor of 10 2.

Abstract

Plasmonic hotspots in single gold nanostars are located at the tips and can be excited selectively by laser light as evidenced by photoelectron emission microscopy. Selectivity is achieved through wavelength and polarization of the excitation light. Comparing photoelectron emission intensity and dark-field scattering spectra of the same individual nanostars reveals differences in terms of observable plasmon resonance wavelengths and field enhancements. Differences are explained with the underlying near- and far-field processes of the two techniques.

Abstract

We present a method to identify the owner of a photo album taken off a social networking site. We consider this as a problem of prominent person mining. We introduce a new notion of prominent persons, and propose a greedy solution based on an eigenface representation. We mine prominent persons in a subset of dimensions in the eigenface space. We present excellent results on multiple datasets downloaded from the Internet. Index Terms—Eigenface, Clustering, Frequent Itemset Mining, Apriori, Prominent Person Mining

Abstract

In this paper, we explain the bag of words representation from a soft computing perspective. The traditional Bag of word representation describes an image as a bag of discrete visual codewords. Where histogram of the number of occurrences of these codewords is used for image classification tasks. The drawback of the approach is that every visual feature in an image is assigned to single codeword, which leads to the loss of information regarding the other relevant codewords that can represent the same feature. In this paper, we show how fuzzy and possibilistic codeword assignment improves the classification performance on Scene-15-dataset.

Abstract

In most of the Optical Character Recognition softwares, a substantial percentage of errors are caused by the incorrect segmentation of degraded words. This is especially true for recognizing old books, newspapers and historical manuscripts. In this paper, we propose multiple segmentation methods which address the problem of cuts and merges in degraded words. We have created an annotated dataset of 1034 word images with pixel level ground truth for quantitative evaluation of the methods. We compare the methods with a baseline implementation based on connected component analysis. We report substantial improvement in accuracy both at character and at word level.

Abstract

Detecting objects in images and videos is very challenging due to i) large intra-class variety and ii) pose/scale variations. It is hard to build strong recognition engines for generic object categories, while applying them to large video collections is computationally infeasible (due to the explosion of frames to test). In this paper, we present a detection-byinterpolation framework, where object-tracking is achieved by interpolating between candidate object detections in a subset of the video frames. Given the location of an object in two frames of a video-shot, our algorithm tries to identify the locations of the object in the intermediate frames. We evaluate two tracking solutions based on greedy and dynamic programming approaches, and observe that a hybrid method gives significant performance boost as well as speedup in detection. On 6 hours of HD quality video, we were able to cut-down the detection time from 10000 hours to 1500 hours, while simultaneously improving the detection accuracy from 54% (of [1]) to 68%. As a result of this work, we build a dataset of 100,000 car images, spanning a wide range of viewpoints, scale and make; about 100 times larger than existing collections

Abstract

Template-based object detectors such as the deformable parts model of Felzenszwalb et al. [11] achieve state-ofthe-art performance for a variety of object categories, but are still outperformed by simpler bag-of-words models for highly flexible objects such as cats and dogs. In these cases we propose to use the template-based model to detect a distinctive part for the class, followed by detecting the rest of the object via segmentation on image specific information learnt from that part. This approach is motivated by two observations: (i) many object classes contain distinctive parts that can be detected very reliably by template-based detectors, whilst the entire object cannot; (ii) many classes (e.g. animals) have fairly homogeneous coloring and texture that can be used to segment the object once a sample is provided in an image. We show quantitatively that our method substantially outperforms whole-body template-based detectors for these highly deformable object categories, and indeed achieves accuracy comparable to the state-of-the-art on the PASCAL VOC competition, which includes other models such as bagof-words.

Abstract

Retrieval from Hindi document image collections is a challenging task. This is partly due to the complexity of the script, which has more than 800 unique ligatures. In addition, segmentation and recognition of individual characters often becomes difficult due to the writing style as well as degradations in the print. For these reasons, robust OCRs are non existent for Hindi. Therefore, Hindi document repositories are not amenable to indexing and retrieval. In this paper, we propose a scheme for retrieving relevant Hindi documents in response to a query word. This approach uses BLSTM neural networks. Designed to take contextual information into account, these networks can handle word images that can not be robustly segmented into individual characters. By zoning the Hindiwords, we simplify the problem and obtain high retrieval rates. Our simplification suits the retrieval problem, while it does not apply to recognition. Our scalable retrieval scheme avoids explicit recognition of characters. An experimental evaluation on a dataset of word images gathered from two complete books demonstrates good accuracy even in the presence of printing variations and degradations. The performance is compared with baseline methods.

Abstract

In this paper, we present a novel approach to search and retrieve from document image collections, without explicit recognition. Existing recognition-free approaches such as word-spotting cannot scale to arbitrarily large vocabulary and document image collections. In this paper we put forth a framework that overcomes three issues of word-spotting: i) retrieving word images not labeled during indexing, ii) allow for query and retrieval of morphological variations of words and iii) scale the retrieval to large collections. We propose a character n-gram spotting framework, where word-images are considered as a bag of visual n-grams. The character n-grams are represented in a visual-feature space and indexed for quick retrieval. In the retrieval phase, the query word is expanded to its constituent n-grams, which are used to query the previously built index. A ranking mechanism is proposed that combines the retrieval results from the multiple lists corresponding to each n-gram. The approach is demonstrated on a size-able collection of English and Malayalam books. With a mean AP of 0.64, the performance of the retrieval system was found to be very promising.

Abstract

Inspired by the success of MRF models for solving object segmentation problems, we formulate the binarization problem in this framework. We represent the pixels in a document image as random variables in an MRF, and introduce a new energy (or cost) function on these variables. Each variable takes a foreground or background label, and the quality of the binarization (or labelling) is determined by the value of the energy function. We minimize the energy function, i.e. find the optimal binarization, using an iterative graph cut scheme. Our model is robust to variations in foreground and background colours as we use a Gaussian Mixture Model in the energy function. In addition, our algorithm is efficient to compute, and adapts to a variety of document images. We show results on word images from the challenging ICDAR 2003 dataset, and compare our performance with previously reported methods. Our approach shows significant improvement in pixel level accuracy as well as OCR accuracy.