Object Detection using Dimensionality Reduction on Image Descriptors

The aim of object detection is to recognize objects in a visual scene. Performing reliable object detection is becoming increasingly important in the fields of computer vision and robotics. Various applications of object detection include video surveillance, traffic monitoring, digital libraries, navigation, human computer interaction, etc. The challenges involved with detecting real world objects include the multitude of colors, textures, sizes, and cluttered or complex backgrounds making objects difficult to detect. This thesis contributes to the exploration of various dimensionality reduction techniques on descriptors for establishing an object detection system that achieves the best trade-offs between performance and speed. Histogram of Oriented Gradients (HOG) and other histogram-based descriptors were used as an input to a Support Vector Machine (SVM) classifier to achieve good classification performance. Binary descriptors were considered as a computationally efficient alternative to HOG. It was determined that single local binary descriptors in combination with Support Vector Machine (SVM) classifier don\u27t work as well as histograms of features for object detection. Thus, histogram of binary descriptors features were explored as a viable alternative and the results were found to be comparable to those of the popular Histogram of Oriented Gradients descriptor. Histogram-based descriptors can be high dimensional and working with large amounts of data can be computationally expensive and slow. Thus, various dimensionality reduction techniques were considered, such as principal component analysis (PCA), which is the most widely used technique, random projections, which is data independent and fast to compute, unsupervised locality preserving projections (LPP), and supervised locality preserving projections (SLPP), which incorporate non-linear reduction techniques. The classification system was tested on eye detection as well as different object classes. The eye database was created using BioID and FERET databases. Additionally, the CalTech-101 data set, which has 101 object categories, was used to evaluate the system. The results showed that the reduced-dimensionality descriptors based on SLPP gave improved classification performance with fewer computations

Image-Based Localization of User-Interfaces

Image localization corresponds to translating the text present in the images from one language to other language. The aim of the project is to develop a methodology to translate the text in image captions from English to Hindi by taking context of the images into account. A lot of work has been done in this field [22], but our aim was to explore if the accuracy can be further improved by consideration of the additional information imparted by the images apart from the text. We have explored Deep Learning using neural networks for this project. In particular, Recurrent Neural Networks (RNN) have been used which are ideal for sequence translations and would meet the needs of this project which involves text sequences. This technique of image localization would be beneficial in a lot of fields. For example, in order to make the text data accessible to everyone, text data should be translated in multiple languages spoken by people across the world. This will help in the growth at the rural areas and countries where English is not spoken by giving them access to data in their local languages. This could also benefit tourists who would then be able to understand the sign boards and posters in a foreign country. With accurate data translation, the old manuscripts can also be translated to English upon which further research can be carried out

DeepPR: Progressive Recovery for Interdependent VNFs with Deep Reinforcement Learning

The increasing reliance upon cloud services entails more flexible networks that are realized by virtualized network equipment and functions. When such advanced network systems face a massive failure by natural disasters or attacks, the recovery of the entire system may be conducted in a progressive way due to limited repair resources. The prioritization of network equipment in the recovery phase influences the interim computation and communication capability of systems, since the systems are operated under partial functionality. Hence, finding the best recovery order is a critical problem, which is further complicated by virtualization due to dependency among network nodes and layers. This paper deals with a progressive recovery problem under limited resources in networks with VNFs, where some dependent network layers exist. We prove the NP-hardness of the progressive recovery problem and approach the optimum solution by introducing DeepPR, a progressive recovery technique based on Deep Reinforcement Learning (Deep RL). Our simulation results indicate that DeepPR can achieve the near-optimal solutions in certain networks and is more robust to adversarial failures, compared to a baseline heuristic algorithm.Comment: Technical Report, 12 page

Gibbs Phenomenon for Jacobi Approximations

The classical Gibbs phenomenon is a peculiarity that arises when approximating functions near a jump discontinuity with the Fourier series. Namely, the Fourier series overshoots (and undershoots ) the discontinuity by approximately 9% of the total jump. This same phenomenon, with the same value of the overshoot, has been shown to occur when approximating jump-discontinuous functions using specific families of orthogonal polynomials. In this paper, we extend these results and prove that the Gibbs phenomenon exists for approximations of functions with interior jump discontinuities with the two-parameter family of Jacobi polynomials Pn(a,b)(x). In particular, we show that for all a, b the approximation overshoots and undershoots the function by the same value as in the classical case – approximately 9% of the jump

PRELIMINARY SCREENING OF PHYTOCHEMICAL COMPONENTS OF PARTHENIUM HYSTEROPHORUS LEAVES AND STUDY OF AUTOTOXIC POTENTIAL OF PARTHENIUM ON ITS MORPHOLOGICAL PARAMETERS

The pot studies were conducted to determine the auto toxic potential of Parthenium hysterophorus leaves on its own morphological parameters. The leaves of Parthenium weed contain several phytochemical components such as phenol compounds, terpenoids and steroids etc. Auto toxicity is a process where a plant or its decomposing residues release toxic chemicals into the environment which may inhibit germination and growth of the same plants. Auto toxicity is closely related to the soil sickness. In the present study the morphological parameters of Parthenium weed such as number of seedlings, number of leaves/plant, plant height, branches/plant, capitula and seeds/plant were significantly inhibited by leaf powder of Parthenium hysterophorus. The reduction in morphological parameters was in the order: T2 treatment > T1treatment > Control. Hence, the auto toxic potential of Parthenium hysterophorus can be utilized as eco-friendly strategy for weed control