代写jsp 最短路径的前端代码、代做数据库操作读写

日期：2018-05-05 04:19

What should you turn in: A single zip file containing: 1) a pdf file with all results and a discussion of your solution 2) all

your resulting images files and code. In addition to the above you should prepare a short (~5min) presentation describing

your work, which you may be required to present at some point.

1) Image Data Association (2.5 Points Max)

The goal of the assignment is to apply an unsupervised clustering algorithm to segregate a set of images observing different

scenes. This is similar to the image classification problem in Assignment 4, in the sense that you need to build a global

image representation and use that representation to associate images. However, instead of assigning a single label from a

predetermined number of possible labels, you will perform unsupervised clustering to partition the set of all images into

disjoint subsets.

a) Download the datasets fountain-P11, Herz-Jesu-P8, entry-P10 from the Strecha MVS evaluation website and

form a single aggregated dataset comprised of all the images in the three datasets (i.e. a superset). For this problem

you will only use the images and discard other info.

b) Extract a global image descriptor for each image. You may choose any available descriptor and are NOT

REQUIRED to develop descriptor code. Example descriptors include color histograms (e.g. Homework 4), GIST,

bag of visual words, etc. Alternatively, you may choose to engineer your own image descriptor if desired.

c) Implement a mean-shift unsupervised clustering algorithm to partition the aggregated dataset into disjoint image

sets. You will need to execute the mean-shift algorithm multiple times using each of the images in the superset as

a starting point and record the convergence “mean” obtained by the algorithm. Images converging to the same

“mean” are considered to belong to the same cluster.

d) Download the Castle-P19 image dataset and aggregate with the entry-P10 image dataset into a new superset.

Perform the same clustering procedure using the same parameters. Please note, there is scene overlap among these

datasets, making correct segregation more ambiguous.

Include in your report the attained clusters; discuss your choice of image descriptor as well as the mechanisms used for

parameter tuning of your clustering algorithm.

2) Segmentation (5 Points Max)

The goal of this assignment is to develop a framework to perform segmentation of a specific foreground object. The object

of interest is a foreground tractor found in the images of the Castle-19 dataset.

The desired output is a pixel-wise segmentation for each of the images in the dataset.

This is an open-ended problem for which we seek a solution that balances segmentation quality with the level of supervision

required to carry out the task. Moreover, in designing your solution you may leverage the shared information among datasets

(e.g. Castle-P19 has common scene regions with entry-P10, while the available Castle-P30 dataset also depicts the

foreground object and can be seen as a superset of Castle-P19), the available 3D ground truth data for each dataset (provided

as a dense 3D mesh), or even elicit user input as part of a semi- automated segmentation framework.

Include in your report the attained segmentation and provide a detailed discussion of your solution.

3) Advanced Image Filtering (7.5 Points Max)

The goal of this assignment is to implement a multi-dimensional Gaussian filter using the Permutohedral Lattice. This

advanced technique was NOT covered in the course slides, hence you will need to research and implement it. Develop a

MATLAB implementation (no other languages will be considered, no exceptions) based on the description in

http://graphics.stanford.edu/papers/permutohedral/

a) Compare results of using a permutohedral-based 2D filter with with spatial std dev of 10 pixels on an RGB image vs

running the typical 2D filter in each channel independently in order to aggregate results.

b) Deploy 5D filtering using pixel color and position (RGB+XY), define spatial std dev of 5, 10, and 10 pixels and RGB

std dev of 10, 20, 30 intensity value. Experiment on any of the training outdoor images of the fourth assignment and test the

nine possible combinations.

Notes: There are existing implementations available online, you are encouraged to use them for benchmarking/testing your

own implementation (if you do so, include the comparative results in your report). The goal of this assignment is NOT for

you to simply translate the code of an existing implementation into MATLAB, but to understand the material and implement

it. There is NO REQUIREMENT to implement data access through a hash table as this is only an efficiency optimization.

Project 4) Non-Iterativre Super-pixel Segmentation (5 points Max)

Implement the paper in the link below

http://openaccess.thecvf.com/content_cvpr_2017/html/Achanta_Superpixels_and_Polygons_

CVPR_2017_paper.html (Links to an external site.)Links to an external site.

and using the imagery from Homework 3, compare against

a) your HW3 approximate implementation of SLIC

b) the original SLIC version available online @

https://ivrl.epfl.ch/research/superpixels

Errata: The link in the pdf document for the Strecha dataset is no longer valid. The

datasets can now be found at