The Achievement of Higher Flexibility in Multiple Choice-based Tests Using Image Classification Techniques

In spite of the high accuracy of the existing optical mark reading (OMR) systems and devices, a few restrictions remain existent. In this work, we aim to reduce the restrictions of multiple choice questions (MCQ) within tests. We use an image registration technique to extract the answer boxes from the answer sheets. Unlike other systems that rely on simple image processing steps to recognize the extracted answer boxes, we address the problem from another perspective by training a classifier to recognize the class of each answer box (i.e., confirmed, crossed out, and blank answer). This gives us the ability to deal with a variety of shading and mark patterns, and distinguish between chosen and canceled answers. All existing machine learning techniques require a large number of examples in order to train the classifier, therefore we present a dataset that consists of six real MCQ assessments that have different answer sheet templates. We evaluate two strategies of classification: a straight-forward approach and a two-stage classifier approach. We test two handcrafted feature methods and a convolutional neural network. At the end, we present an easy-to-use graphical user interface of the proposed system. Compared with existing OMR systems, the proposed system has a higher accuracy and the least constraints. We believe that the presented work will further direct the development of OMR systems towards reducing the restrictions of the MCQ tests.

Semantic White Balance: Semantic Color Constancy Using Convolutional Neural Network

The goal of computational color constancy is to preserve the perceptive colors of objects under different lighting conditions by removing the effect of color casts caused by the scene's illumination. With the rapid development of deep learning based techniques, significant progress has been made in image semantic segmentation. In this work, we exploit the semantic information together with the color and spatial information of the input image in order to remove color casts. We train a convolutional neural network (CNN) model that learns to estimate the illuminant color and gamma correction parameters based on the semantic information of the given image. Experimental results show that feeding the CNN with the semantic information leads to a significant improvement in the results by reducing the error by more than 40%.

Image Posterization Using Fuzzy Logic and Bilateral Filter

Image posterization is converting images with a large number of tones into synthetic images with distinct flat areas and a fewer number of tones. In this technical report, we present the implementation and results of using fuzzy logic in order to generate a posterized image in a simple and fast way. The image filter is based on fuzzy logic and bilateral filtering; where, the given image is blurred to remove small details. Then, the fuzzy logic is used to classify each pixel into one of three specific categories in order to reduce the number of colors. This filter was developed during building the Specs on Face dataset in order to add a new level of difficulty to the original face images in the dataset. This filter does not hurt the human detection performance; however, it is considered a hindrance evading the face detection process. This filter can be used generally for posterizing images, especially those have a high contrast to get images with vivid colors.

Can We Boost the Power of the Viola-Jones Face Detector Using Pre-processing? An Empirical Study

The Viola-Jones face detection algorithm was (and still is) a quite popular face detector. In spite of the numerous face detection techniques that have been recently presented, there are many research works that are still based on the Viola-Jones algorithm because of its simplicity. In this paper, we study the influence of a set of blind pre-processing methods on the face detection rate using the Viola-Jones algorithm. We focus on two aspects of improvement, specifically badly illuminated faces and blurred faces. Many methods for lighting invariant and deblurring are used in order to improve the detection accuracy. We want to avoid using blind pre-processing methods that may obstruct the face detector. To that end, we perform two sets of experiments. The first set is performed to avoid any blind pre-processing method that may hurt the face detector. The second set is performed to study the effect of the selected pre-processing methods on images that suffer from hard conditions. We present two manners of applying the pre-processing method to the image prior to being used by the Viola-Jones face detector. Four different datasets are used to draw a coherent conclusion about the potential improvement caused by using prior enhanced images. The results demonstrate that some of the pre-processing methods may hurt the accuracy of Viola-Jones face detection algorithm. However, other pre-processing methods have an evident positive impact on the accuracy of the face detector. Overall, we recommend three simple and fast blind photometric normalization methods as a pre-processing step in order to improve the accuracy of the pre-trained Viola-Jones face detector.

AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces

Gender classification aims at recognizing a person's gender. Despite the high accuracy achieved by state-of-the-art methods for this task, there is still room for improvement in generalized and unrestricted datasets. In this paper, we advocate a new strategy inspired by the behavior of humans in gender recognition. Instead of dealing with the face image as a sole feature, we rely on the combination of isolated facial features and a holistic feature which we call the foggy face. Then, we use these features to train deep convolutional neural networks followed by an AdaBoost-based score fusion to infer the final gender class. We evaluate our method on four challenging datasets to demonstrate its efficacy in achieving better or on-par accuracy with state-of-the-art methods. In addition, we present a new face dataset that intensifies the challenges of occluded faces and illumination changes, which we believe to be a much-needed resource for gender classification research.

Can We See Photosynthesis? Magnifying the Tiny Color Changes of Plant Green Leaves Using Eulerian Video Magnification

Plant aliveness is proven through laboratory experiments and special scientific instruments. In this paper, we aim to detect the degree of animation of plants based on the magnification of the small color changes in the plant's green leaves using the Eulerian video magnification. Capturing the video under a controlled environment, e.g., using a tripod and direct current (DC) light sources, reduces camera movements and minimizes light fluctuations; we aim to reduce the external factors as much as possible. The acquired video is then stabilized and a proposed algorithm used to reduce the illumination variations. Lastly, the Euler magnification is utilized to magnify the color changes on the light invariant video. The proposed system does not require any special purpose instruments as it uses a digital camera with a regular frame rate. The results of magnified color changes on both natural and plastic leaves show that the live green leaves have color changes in contrast to the plastic leaves. Hence, we can argue that the color changes of the leaves are due to biological operations, such as photosynthesis. To date, this is possibly the first work that focuses on interpreting visually, some biological operations of plants without any special purpose instruments.