Land cover classification of landsat thematic mapper images using pseudo invariant feature normalization applied to change detection

A radiometric normalization technique for compensating illumination and atmospheric differences between multi-temporal images should allow classification of the images with a single classification algorithm. This allows a simpler approach to land cover change detection. Land cover classification of Landsat Thematic Mapper Imagery with and without Pseudo Invariant Feature Normalization was performed to demonstrate the effect on classification and change detection accuracy. A post-classification change detection method using two separate classification algorithms, one for each date, was performed as a baseline comparison. Land cover classification using one classification algorithm was attempted with and without gain and offset correction to serve as another comparison. Accuracy verification was performed on the classification results by comparing random samples against ground truth

Deep learning for facial emotion recognition

The ability to perceive and interpret human emotions is an essential as-pect of daily life. The recent success of deep learning (DL) has resulted in the ability to utilize automated emotion recognition by classifying af-fective modalities into a given emotional state. Accordingly, DL has set several state-of-the-art benchmarks on static affective corpora collected in controlled environments. Yet, one of the main limitations of DL based intelligent systems is their inability to generalize on data with nonuniform conditions. For instance, when dealing with images in a real life scenario, where extraneous variables such as natural or artificial lighting are sub-ject to constant change, the resulting changes in the data distribution commonly lead to poor classification performance. These and other con-straints, such as: lack of realistic data, changes in facial pose, and high data complexity and dimensionality increase the difficulty of designing DL models for emotion recognition in unconstrained environments. This thesis investigates the development of deep artificial neural net-work learning algorithms for emotion recognition with specific attention to illumination and facial pose invariance. Moreover, this research looks at the development of illumination and rotation invariant face detection architectures based on deep reinforcement learning. The contributions and novelty of this thesis are presented in the form of several deep learning pose and illumination invariant architectures that offer state-of-the-art classification performance on data with nonuniform conditions. Furthermore, a novel deep reinforcement learning architecture for illumination and rotation invariant face detection is also presented. The originality of this work is derived from a variety of novel deep learning paradigms designed for the training of such architectures

Large scale evaluation of local image feature detectors on homography datasets

We present a large scale benchmark for the evaluation of local feature detectors. Our key innovation is the introduction of a new evaluation protocol which extends and improves the standard detection repeatability measure. The new protocol is better for assessment on a large number of images and reduces the dependency of the results on unwanted distractors such as the number of detected features and the feature magnification factor. Additionally, our protocol provides a comprehensive assessment of the expected performance of detectors under several practical scenarios. Using images from the recently-introduced HPatches dataset, we evaluate a range of state-of-the-art local feature detectors on two main tasks: viewpoint and illumination invariant detection. Contrary to previous detector evaluations, our study contains an order of magnitude more image sequences, resulting in a quantitative evaluation significantly more robust to over-fitting. We also show that traditional detectors are still very competitive when compared to recent deep-learning alternatives.Comment: Accepted to BMVC 201

Deteksi Perubahan Citra Pada Video Menggunakan Illumination Invariant Change Detection

 There is still a lot of juvenile delinquency in the middle of the community, especially people in urban areas, in the modern era. Juvenile delinquency may be fights, wild racing, gambling, and graffiti on the walls without permission. Vandalized wall is usually done on walls of office buildings and on public or private property. Results from vandalized walls can be seen from the image of the change between the initial image with the image after a motion.This study develops a image change detection system in video to detect the action of graffiti on the wall via a Closed-Circuit Television camera (CCTV) which is done by simulation using the webcam camera. Motion detection process with Accumulative Differences Images (ADI) method and image change detection process with Illumination Invariant Change Detection method coupled with image cropping method which carried out a comparison between the a reference image or image before any movement with the image after there is movement.Detection system testing one by different times variations, ie in the morning, noon, afternoon, and evening. The proposed method for image change detection in video give results with an accuracy rate of 92.86%