Description: 运用harr特征+SRC（稀疏表示）分类实现的一种车辆检测方法，文件中提供了训练和测试车辆图片。由于时间原因，所用haar特征没有优化，维度过高，导致滑窗框图过慢，本代码只输出效果统计数据，以供大家参考学习稀疏表示在车辆检测中的应用。-Using harr feature+SRC (sparse representation) classification to achieve a vehicle detection method, the paper provides a training and test vehicle picture. Due to time reasons, the use of haar feature is not optimized, high dimension, resulting in sliding sash figure is too slow, the effect of the code only output statistics for your reference learning sparse representation in the vehicle detection. Platform: |
Size: 11820032 |
Author:高晨旭 |
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Description: 这是SPAMS的原始代码，是稀疏表示的重要工具箱~-This is SPAMS original code, is an important toolbox sparse representation Platform: |
Size: 1485824 |
Author:韩雪 |
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Description: 本程序适用于论文metasample-based sparse representation for tumor classification-this code suite for paper metasample-based sparse representation for tumor classification Platform: |
Size: 445440 |
Author:甘斌 |
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Description: 此程序为袁晓彤CVPR论文“基于多任务联合稀疏表示的的视觉分类”的主代码部分-The program for Yuan Xiaotong CVPR paper " visual classification with Muti-task joint sparse representation" main code of the parts Platform: |
Size: 12288 |
Author:郑秋忠 |
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Description: Face recognition using L1 norm minimization 1.0 :Read the following paper for details of the algorithm - Robust Face Recognition via Sparse Representation by John Wright, Arvind Ganesh, and Yi Ma , Coordinated Science Laboratory, University of Illinois at Urbana-Champaign and Allen Yang, Electrical Engineering and Computer Science, University of California Berkeley. Platform: |
Size: 4096 |
Author:thaslee |
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Description: 基于小波变换和OMP算法的稀疏表示,不仅包括适用于黑白图片的程序，也包括适用于彩色图片的程序。-the sparse representation basing on wavelet and omp,not only for the gray image,but also color image
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Size: 369664 |
Author:赵 |
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Description: 曲波变换是图像处理领域中稀疏表示最常用的一种字典，其中MCA分解模型中经常用到。-Bo transform the field of image processing is the most common kind of sparse representation dictionary MCA decomposition model which is often used. Platform: |
Size: 122880 |
Author:赵 |
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Description: 此程序，是应用在压缩感知方面的稀疏表示，图像重构。程序中利用现在流行的CS理论，在采样率为0.9时，成功的实现了图像重构-This procedure is used in the compressed sensing aspects of sparse representation, image reconstruction. Program using the now popular CS theory, the sampling rate is 0.9, the successful realization of the image reconstruction Platform: |
Size: 2048 |
Author:hanguohua |
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Description: SRC。基于稀疏表示的人脸识别。其中有我自己改动的部分。-SRC. Face recognition based on sparse representation. Changes which have my own part. Platform: |
Size: 16884736 |
Author:何双 |
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Description: 清华大学2013年针对稀疏表示的一个讲座，请的是稀疏表示的创始人。其中为讲座的讲义。-Tsinghua University in 2013 for the sparse representation of a lecture, please sparse representation is the founder. Where is the lecture handouts. Platform: |
Size: 6591488 |
Author:何双 |
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Description: 基于稀疏表示的正交最小二乘法，使用的语言是matlab，应用比较广阔，设计信号处理中的信号回归，图像处理的压缩等。-Sparse representation based on orthogonal least squares method, the language used is matlab, relatively broad application, design signal processing signal return, image processing, compression and so on. Platform: |
Size: 16384 |
Author:庞枫骞 |
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Description: 针对稀疏表示识别方法需要大量样本训练过完备字典且特征冗余度较高的问题，提出了结合过完备字典学习与PCA降维的小样本语音情感识别算法.该方法首先用PCA降维方法将特征降维，再将处理后的特征用于过完备字典训练与稀疏表示识别方法，从而给出了语音情感特征的稀疏表示方法，并确定了新算法的具体步骤.为验证其有效性，在同等特征维数下，将方法与BP, SVM进行比较，并对比、分析语音情感特征稀疏化前后对语音情感识别率、时间效率以及空间效率的影响.试验结果表明，所提出方法的识别率比SVM与BP高 与采用稀疏化前的特征相比，稀疏化后的特征向量更便于处理，平均识别率提高约15 ，时间效率提高近原来的1 /2，空间效率提升近原来的1 /3.
-Identification methods for sparse representation requires a lot of training samples and high over-complete dictionary feature redundancy problem, a combination of over-complete dictionary learning and PCA dimension small sample speech emotion recognition algorithms. Firstly, the PCA dimension reduction methods feature reduction, feature and then treatment for the over-complete dictionary training and recognition sparse representation, which gives a speech emotion feature sparse representation, and to determine the specific steps of the new algorithm. To verify its validity, in Under the same number of features, the method and BP, SVM compare and contrast, analyze the impact before and after the speech emotion feature sparse speech emotion recognition rate, time-efficient and space-efficient. experimental results show that the recognition rate of the proposed method than High SVM and BP compared to pre-thinning characteristics using eigenvectors easier after thinning processing, the av Platform: |
Size: 629760 |
Author:wangming |
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