1000 Fps Highly Accurate Eye Detection with Stacked Denoising Autoencoder
نویسندگان
چکیده
Eye detection is an important step for a range of applications such as iris and face recognition. For eye detection in practice, speed is as equally important as accuracy. In this paper, we propose a super-fast (1000 fps on a general PC) eye detection method based on the label map of the raw image without face detection. We firstly produce the label map of a raw image according to the coordinates of its bounding box . Then we train a stacked denoising autoencoder (SDAE) which is specifically designed to learn the mapping from the raw image to the label map. Finally, through an effective post-processing step, we obtain the bounding boxes of two eyes. Experimental results show that our method is about 2,500 times faster than the deformable part-based model (DPM) while maintaining a comparable accuracy. Also, our method is much better than the popular LBP+Cascade model in terms of both accuracy and speed.
منابع مشابه
Decoding Stacked Denoising Autoencoders
Data representation in a stacked denoising autoencoder is investigated. Decoding is a simple technique for translating a stacked denoising autoencoder into a composition of denoising autoencoders in the ground space. In the infinitesimal limit, a composition of denoising autoencoders is reduced to a continuous denoising autoencoder, which is rich in analytic properties and geometric interpretat...
متن کاملRobust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders
Computer-aided diagnosis (CAD) is a promising tool for accurate and consistent diagnosis and prognosis. Cell detection and segmentation are essential steps for CAD. These tasks are challenging due to variations in cell shapes, touching cells, and cluttered background. In this paper, we present a cell detection and segmentation algorithm using the sparse reconstruction with trivial templates and...
متن کاملA Deep Learning Approach for Cancer Detection and Relevant Gene Identification
Cancer detection from gene expression data continues to pose a challenge due to the high dimensionality and complexity of these data. After decades of research there is still uncertainty in the clinical diagnosis of cancer and the identification of tumor-specific markers. Here we present a deep learning approach to cancer detection, and to the identification of genes critical for the diagnosis ...
متن کاملA Template-Based Protein Structure Reconstruction Method Using Deep Autoencoder Learning
Protein structure prediction is an important problem in computational biology, and is widely applied to various biomedical problems such as protein function study, protein design, and drug design. In this work, we developed a novel deep learning approach based on a deeply stacked denoising autoencoder for protein structure reconstruction. We applied our approach to a template-based protein stru...
متن کاملDeep Similarity Learning for Multimodal Medical Images
An effective similarity measure for multi-modal images is crucial for medical image fusion in many clinical applications. The underlining correlation across modalities is usually too complex to be modelled by intensity-based statistical metrics. Therefore, approaches of learning a similarity metric are proposed in recent years. In this work, we propose a novel deep similarity learning method th...
متن کامل