Hey. I have made an IOS and Android app that solves any homework from pictures and if any of you are in college or school, I would be happy to get your review on it. It is called TaskGuru - AI Chat & Photo on both AppStore and Google Play.
In some areas of an Ultra course, you can use the editor functions to add images along with text content. You can add images that are hosted online, in cloud storage, or from your local drive. You can't add images in the editor in calendar items.
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Add images from the web. Select Insert Content (the plus sign icon) > Image. Type or paste an image URL to embed an image hosted online. You must use the http:// protocol. Include a description of the image in the Alternative text box for screen readers and users who can't view the image.
You will be responsible for digitizing at least ten photographs or images via a digital camera or scanner. You should then store these images in at least two resolutions. Ideally, use a digital camera for up to 9 of the images, but use a scanner for converting at least one print image to digital form to give yourself that experience as well.Make sure at least 3 of the images contain some text on the image .These images may also become part of the interactive animation you will create as the next homework assignment, and/or part of the web site you turn in at the end of the course.
Using any other images EXCEPT your own, try to find seven of the images that you had scanned in. What settings, parameters did you use to find the image? In your homework web page, list the query image you used (if any), the target image from your set, and which QBIC parameter settings were used to find the target (specify parameter settings clearly and completely so that search can be repeated by someone else). You get 3 points for each of the 10 images scanned in or digitized well (good choice of cropping, orientation, and scanner/image source free of dirt/lint/etc.) (30 points total), and you will get 10 points for each target found as a first choice in a query (7 queries at 10 points each) for a total of up to 100 points. Target images found as 2nd choice would give 8 points, 3rd choice 6 points, 4th choice 4 points and 5th choice 2 points. You will receive only 1 point if your target image is in the top 10 results but not in the top 5. You can only use "Customized Search" (where you pick colors and/or color layouts) for 4 of your queries; the other 3 must be based on a query image selected from the QBIC image set, where you select Color Percentages, Color Layout, or Texture as the similarity measure and then click on the image located in the first upper left slot to do a similarity search against that image. When you use an image key (search against an image), the answers are then listed in order next to that image key, so of course for such searches you're trying to get your target image to be listed in the second slot next to the image key, which counts as being the first choice for the query.
You must have your images scanned in/digitized and ftp'd over by 6 a.m. October 31. Some time on October 31 the QBIC indices will be built. Your homework web pages must be completed by 11:59 p.m. November 4. NOTE: The QBIC web server (now offline) is limited to 10 simultaneous connections at a time, so you will need to stagger your usage. Cooperate amongst yourselves so that you won't be frustrated with "Cannot access the server...." messages which will happen if more than 10 of you try to download to or access the server at the same time.
The zip file contains 4 folders. The 'Code' folder contains the code files, the 'Database' folder contain the database images in .ppm format (You can use IrfanView to view such images), the 'ImageView' folder contain the same images as in the Database folder but in .jpg format and the 'Thumbnails' folder contains the thumbnails of the database images to be used to display the results. The images in 'ImageView' are just for your viewing, code is written so as to read the .ppm images and display the thumbnail images in the application window after executing the code. For this project, significant parts of mainwindow.cpp and mainwindow.h files have been changed, so you may want to take a look at these apart from the Project3.cpp file which you need to edit. Please read all the comments in Project3.cpp file carefully before you start writing code. If you want to scale up your application window for better viewing, refer to the 2 commented lines in the main.cpp file. (1) After opening the project file in Qt, make sure that 'Projects' tab -> 'Build Settings' -> 'Shadow build' checkbox is unchecked, otherwise the project will not build. (2) Under 'Projects' tab -> 'Run Settings' (Click 'Run' under 'Build and Run' in the left pane), select the 'Code' folder as the current 'Working directory'. (3) This homework deals with images in QImage format.
The main idea is to represent each image (the database images and the query image) by a set of regions obtained from color clustering and connected components and extract the region attributes. Then, come up with a distance function between two images in this representation, and use this distance function to find the images most similar to a query image.
In order to test your system, use the following images as the query image: beach_2 boat_5 cherry_3 crater_3 pond_2 stHelens_2 sunset1_2 sunset2_2
As an instructor, you can copy individual assignments in one course directly into any active course in which you are enrolled. You can also send assignments to other instructors at your institution. When you copy an assignment, any assets within that assignment (images, files, etc.) will be included in your assignment copy.
Even though your parents probably complain about how hard it was in their day, students nowadays have more homework than ever before, even when just starting their first year at middle school. That homework doesn't need to be a struggle now. Learning to plan out an efficient schedule for completing your homework, working on it effectively, and knowing when to get help with difficult assignments can help take the stress out of studying. Don't put it off any longer. See Step 1 for more information.
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Note: Image changes are saved to Manage Files at the course-level root, or, depending on user permissions, as new file attachments to the item (such as the discussion) every time a change is made. Multiple edits, in turn, create multiple files. If the course is copied, all image edits are copied as well. Edited images are saved with their original names followed by a date-time string, which helps to find changed pictures easily when browsing files in Manage Files.
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III. Use the links in #2, above, to count craters in the at leastfive of the images preselected. Use the fullmoon image to count the largest craters, taking care not to doublecount any moderate size craters already counted in one of the close-upimages.
Homework grading is critical to evaluate teaching quality and effect. However, it is usually time-consuming to grade the homework manually. In automatic homework grading scenario, many optical mark reader (OMR)-based solutions which require specific equipments have been proposed. Although many of them can achieve relatively high accuracy, they are less convenient for users. In contrast, with the popularity of smart phones, the automatic grading system which depends on the image photographed by phones becomes more available. In practice, due to different photographing angles or uneven papers, images may be distorted. Moreover, most of images are photographed under complex backgrounds, making answer areas detection more difficult. To solve these problems, we propose BAGS, an automatic homework grading system which can effectively locate and recognize handwritten answers. In BAGS, all the answers would be written above the answer area underlines (AAU), and we use two segmentation networks based on DeepLabv3+ to locate the answer areas. Then, we use the characters recognition part to recognize students' answers. Finally, the grading part is designed for the comparison between the recognized answers and the standard ones. In our test, BAGS correctly locates and recognizes the handwritten answers in 91% of total answer areas.
This goal of the homework is to identify at least 4 different hand gestures in a video. The algorithm should be able to recognize one hand gesture, two hands gestures and if hand is waving. The result is important as we can apply this algorithm to situations where only video input is available. Because our algorithm is dependent on skin color detection, we need to make the assumption that the background contains less or no segments of skin color. We also need to make sure the environment is properly supplied with natural light, as the color captured by the camera will deviate if different wavelength of light is projected on to the object. ff782bc1db
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